CN113709599A - Data processing method and equipment configuration for edge calculation of intelligent instrument - Google Patents
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Abstract
The invention relates to an intelligent instrument edge calculation data processing method and equipment configuration, wherein the method comprises the following steps: step 1, initializing equipment, and carrying out conventional detection on an intelligent instrument, wherein the sampling frequency of the intelligent instrument in the conventional detection process is a first sampling frequency; step 2, judging whether at least one of the intelligent meters detected aiming at the plurality of parameters reaches a first threshold condition; if yes, entering step 3, if not, continuing step 2; step 3, setting the sampling frequency detected by the intelligent instrument as a second sampling frequency; and 4, respectively storing and transmitting the first sampling frequency data and the second sampling frequency data, and integrating the first sampling frequency data and the second sampling frequency data at a cloud layer.
Description
Technical Field
The invention relates to a data processing method, in particular to a data processing method and equipment configuration for edge calculation of an intelligent instrument.
Background
The urban gas pipe network has the characteristics of wide geographical distribution, multiple equipment types, multiple network connections, variable operation modes and the like. With the popularization and application of gas supply automation, production management systems, advanced measurement systems, gas quality monitoring systems and user energy efficiency management systems, the generated heterogeneous and multivariate data grows exponentially, and the data volume reaches a big data level. The traditional cloud computing is gradually restricted in application in urban gas pipe networks due to the limitation of data transmission.
In recent years, edge computing has attracted attention, and is a novel computing model for performing computing at the edge of a network, the objects of edge computing operation include downlink data from cloud services and uplink data from internet of everything services, and the edge of edge computing refers to any computing and network resource between paths from a data source to a cloud computing center. The edge calculation is a distributed open platform which integrates network, calculation, storage and application core capabilities at the edge side of a network close to an object or a data source, edge intelligent services are provided nearby, and key requirements of industry digitization on aspects of agile connection, real-time business, data optimization, application intelligence, safety, privacy protection and the like are met. It can be used as a bridge to connect physical and digital worlds, enabling intelligent assets, intelligent gateways, intelligent systems and intelligent services.
However, in the prior art, the detection end of the edge calculation is usually in a long-term monitoring state, so the sampling frequency is usually set to a conventional detection frequency with a lower frequency, which is busy, not easy to find instantaneous parameter fluctuation, and easy to miss characteristic data in the case of specific parameter change. For gas network systems, transient parameter condition fluctuations or peak data in certain cases are of great importance for the state analysis thereof. The omission of the data can cause the reduction of the monitoring accuracy of the subsequent gas pipe network safety or other states.
Therefore, it is necessary to provide a data processing method and device configuration for edge computing of an intelligent instrument, which can adjust the detection state of a sensor for the whole pipe network state, and perform packet processing, transmission and integration for data in different detection states, so as to ensure the scientificity and accuracy of monitoring the network management state for the intelligent instrument of the urban gas pipe network under the conditions of higher overall efficiency of the edge computing device and lower occupied resources.
Disclosure of Invention
The technical problem to be solved by the invention is the following defects in the prior art: in the prior art, the detection end of the edge calculation is usually in a long-term monitoring state, so the sampling frequency is usually set to be a conventional detection frequency with a lower frequency, the lower frequency is busy, the instantaneous parameter fluctuation is not easy to be found, and on the other hand, the characteristic data under the condition of specific parameter change is easy to miss. For gas network systems, transient parameter condition fluctuations or peak data in certain cases are of great importance for the state analysis thereof. The omission of the data can cause the reduction of the monitoring accuracy of the subsequent gas pipe network safety or other states.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for processing data for intelligent meter edge calculation, the method comprising:
step 1, initializing equipment, and carrying out conventional detection on an intelligent instrument, wherein the sampling frequency of the intelligent instrument in the conventional detection process is a first sampling frequency;
step 2, judging whether at least one of the intelligent meters detected aiming at the plurality of parameters reaches a first threshold condition; if yes, entering step 3, if not, continuing step 2;
step 3, setting the sampling frequency detected by the intelligent instrument as a second sampling frequency;
and 4, respectively storing and transmitting the first sampling frequency data and the second sampling frequency data, and integrating the first sampling frequency data and the second sampling frequency data at a cloud layer.
Specifically, the second sampling frequency is greater than the first sampling frequency.
Specifically, the second sampling frequency is 10-30 times the first sampling frequency.
Specifically, the first sampling frequency data includes first sampling frequency detection data of the sensor and first time series data corresponding to the first sampling frequency detection data.
Specifically, the second sampling frequency data includes second sampling frequency detection data of the sensor and second time series data corresponding to the second sampling frequency detection data.
Specifically, the first time series data and the second time series data are calibrated according to the time sequence of actual detection.
Specifically, the first and second time series data in the first and second sampling frequency data are continuous first and second sampling frequency data, and are transmitted as an independent data packet.
Specifically, the method further comprises: determining whether all of the intelligent meters detected for the plurality of parameters meet a second threshold condition; if yes, go to step 1, if no, continue to step 3.
Specifically, when the cloud layer needs to read the measurement data, the data of the data storage module is sent to the cloud storage module of the cloud layer.
The configuration is used for executing the data processing method for the edge calculation of the intelligent instrument, and a detection layer, an edge layer, a transmission layer and a cloud end layer are connected in sequence through a communication means.
The method and the equipment configuration for processing the edge calculation data of the intelligent instrument have the following beneficial effects: the detection state of the sensor can be adjusted according to the whole network state of the pipe, and the data in different detection states are grouped, processed, transmitted and integrated, so that the scientificity and the accuracy of the intelligent instrument of the urban gas pipe network for monitoring the network state are guaranteed under the conditions that the overall efficiency of the edge computing equipment is higher and the occupied resources are lower. The method specifically comprises the following steps:
1) by reasonably and scientifically setting the mathematical range of the floating threshold values of the absolute values of the temperature and the flow and the sudden change threshold value of the slope change rate of the pressure curve, the sensitivity of the intelligent instrument in the edge computing system to the fluctuation of the sensitive parameters of the pipe network can be improved, and the problem that the intelligent instrument cannot respond in time when the sensitive parameters generate slight fluctuation is solved.
2) When the sensitive parameters of the pipe network meet the threshold conditions, the sampling frequency of the pipe network parameters is improved, the problems that instantaneous parameter fluctuation is not easy to find and characteristic data under the condition of specific parameter change is omitted due to the fact that the sampling frequency of the intelligent instrument is low in normal state monitoring are solved, and the accuracy of monitoring the subsequent safety or other states of the gas pipe network is improved.
3) The first sampling frequency data and the second sampling frequency data are stored in a classified mode and are transmitted respectively, and the data are transmitted to the cloud storage module earlier through the transmission layer link path design, the fact that the data with lower time sequence data (detected earlier) can be transmitted to the cache of the cloud storage module earlier is guaranteed, the early data receiving and caching of the data with the earlier detection are avoided, large-scale data sequence rearrangement adjustment is avoided, occupation of system resources in the cloud storage module during data sequencing and rearrangement is reduced, the overall efficiency of edge computing equipment is higher, and under the condition that the occupied resources are lower, the scientificity and accuracy of the urban gas pipe network intelligent instrument for monitoring the state of the pipe network are guaranteed.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent instrument edge computing system for an urban gas pipeline network according to the present application.
Fig. 2 is a node structure of a transmission layer of the device configuration provided in the present application.
Detailed Description
The present invention will now be described in more detail with reference to the accompanying drawings, in which preferred embodiments of the invention are shown, it being understood that one skilled in the art may modify the invention herein described while still achieving the beneficial results of the present invention. Accordingly, the following description should be construed as broadly as possible to those skilled in the art and not as limiting the invention.
In the interest of clarity, not all features of an actual implementation are described. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific details must be set forth in order to achieve the developer's specific goals.
In order to make the objects and features of the present invention more comprehensible, embodiments of the present invention are described in detail below with reference to the accompanying drawings. It is to be noted that the drawings are in a very simplified form and are intended to use non-precision ratios for the purpose of facilitating and clearly facilitating the description of the embodiments of the invention.
The application provides a data processing method and equipment configuration for edge calculation of an intelligent instrument.
The equipment configuration comprises a detection layer, an edge layer, a transmission layer and a cloud end layer which are sequentially connected through a communication means. The communication means may be a wired network, a wireless network, etc.
The detection layer includes a plurality of sensors including a 1 st sensor, a 2 nd sensor, … …, an nth sensor. Each of the plurality of sensors is used to measure a different pipe section and/or a different physical quantity, respectively. Each of the plurality of sensors is connected to the edge layer through a network, and the real-time data of each of the plurality of sensors is stored in one edge storage node of the edge layer.
The edge layer comprises a data acquisition module, a data storage module, a data analysis processing module, an execution module and a sampling frequency adjusting module. The data acquisition module is used for acquiring and reading real-time data from a plurality of sensors of the detection layer and performing analog-to-digital conversion if necessary.
The data storage module is used for storing data transmitted by the detection layer and comprises a plurality of storage nodes in one-to-one correspondence with the plurality of sensors. Each storage node comprises a first storage module and a second storage module. The first storage module and the second storage module are respectively used for storing real-time data collected by the same sensor under different sampling frequencies.
The data processing module is used for detecting the data in the data storage module in real time so as to judge the execution conditions of various controls, and further controls the execution module to send specific driving control signals to the downstream components controlled by the execution module when the execution conditions are reached. The downstream components include the various sensors of the detection layer and a sampling frequency adjustment module.
The sampling frequency adjusting module is used for adjusting the sampling frequency of each sensor according to the instruction sent by the data executing module, so that the sensor acquisition data with the adjustable sampling frequency can be obtained under the specific condition.
The data are uploaded to the cloud end layer by the storage nodes of the data storage module through the transmission layer, and therefore computing analysis of the cloud end computing device on the data of the storage nodes of the data storage module is achieved. Specifically, different paths of data passing through the network nodes in the transport layer are set according to the data storage positions (namely, the data are stored in the first storage module or the second storage module) in the storage nodes of the data storage module and the network condition.
The cloud layer comprises a cloud storage module and a cloud computing module (not shown), and the cloud layer is used for receiving data of the data storage module through the transmission layer and performing computing analysis on storage node data of the data storage module through the cloud computing device.
The transmission layer is a transmission network formed by N nodes, and different transmission node paths can be selected in the transmission layer according to different storage module positions and network conditions.
The application also provides a data processing method for the edge calculation of the intelligent instrument based on the equipment configuration, which comprises the following steps:
the method comprises the steps that firstly, a system is initialized, the sampling frequency of a sampling frequency adjusting module is set to be a first sampling frequency f1, and a data acquisition module of an edge layer is used for acquiring first sampling frequency data of each sensor and storing the first sampling frequency data in a first storage module of a storage node corresponding to the sensors one by one.
The first sampling frequency data includes first sampling frequency detection data of the sensor and first timing data corresponding to the first sampling frequency detection data. The first time sequence data can correspond to the corresponding first sampling frequency detection data and can represent the time points of detection of the plurality of first sampling frequency detection data.
Wherein the sensor comprises: temperature sensors, gas pressure sensors, gas flow sensors, etc. The sensors can be installed at the inlet/outlet of the pressure regulating station, the main trunk line of the community pipe network, the branch pipes of the community pipe network, and preferably the client ends of the branch pipes of the community pipe network.
And step two, the data processing module detects the data in the data storage module in real time and detects whether the real-time data reaches a condition of starting a second sampling frequency. If the condition of starting the second sampling frequency is met, the execution module sends a driving control signal to the sampling frequency adjustment module, adjusts the sampling frequency of the plurality of sensors to be the second sampling frequency, wherein the second sampling frequency f2 is greater than the first sampling frequency f1, specifically, the second sampling frequency f2 is n times of the first sampling frequency f1, wherein n is an integer of 10-30, preferably n is an integer of 10-20, and executes the third step. And if the condition of starting the second sampling frequency is not met, keeping the first sampling frequency condition, and continuously executing the real-time detection of the step two.
Specifically, the starting of the second sampling frequency condition includes: at least one of the following three conditions is satisfied: 1) real-time temperature satisfaction detected by temperature sensor
Wherein Ti is the temperature data collected in real time, i is the ith time point,
is the average of the 1-i temperature data, TjAll data collected at 1 st to i th time collection points are shown; and/or 2) the pressure sensor detects the data in real time to meet
Wherein P (t)i) Rho is the natural gas density and g is the gravity acceleration of the real-time collected air pressure data; and/or 3) real-time detection data satisfaction of flow sensor
Li is the flow data collected in real time, i is the ith time point,
is the average value of 1-i flow data, LjAll the flow data collected at the 1 st to i th time collection points are shown. When at least one of the above conditions 1) -3) is satisfied, it is determined that the start second sampling frequency condition is reached.
And thirdly, acquiring data of the n sensors by adopting a second sampling frequency, acquiring second sampling frequency data of each of the plurality of sensors by the data acquisition module of the edge layer, and storing the second sampling frequency data in a second storage module of the storage node corresponding to the sensors one by one.
The second sampling frequency data includes second sampling frequency detection data of the sensor and second timing data corresponding to the second sampling frequency detection data. The second time series data can correspond to the corresponding second sampling frequency detection data and can represent the time points of detection of a plurality of second sampling frequency detection data. And the first time series data and the second time series data are calibrated according to the time sequence of actual detection.
And step four, detecting that the second sampling frequency condition exits, judging that the second sampling frequency condition exits is reached when the second sampling frequency condition is met, executing the step one, and continuing to execute the step three if the second sampling frequency condition does not exit.
The exit second sampling frequency condition is that the following three conditions are simultaneously satisfied: 1) real-time temperature satisfaction detected by temperature sensor
Wherein Ti is the temperature data collected in real time, i is the ith time point,
is the average of the 1-i temperature data, TjAll data collected at 1 st to i th time collection points are shown; and 2) the real-time detection data of the pressure sensor meet
Wherein P (t)i) Rho is the natural gas density and g is the gravity acceleration of the real-time collected air pressure data; and 3) the real-time detection data of the flow sensor meet
Li is the flow data collected in real time, i is the ith time point,
is the average value of 1-i flow data, LjAll the flow data collected at the 1 st to i th time collection points are shown. And when the three conditions are simultaneously met, judging that the second sampling frequency condition is met.
And fifthly, when the cloud layer needs to read the measured data, sending the data of the data storage module to the cloud storage module of the cloud layer. Referring to fig. 2, the method specifically includes:
step 5.1, when the cloud layer needs to read the measurement data, the cloud issues a data request to a specific edge node of the data storage module;
and 5.2, the edge node receiving the data sending request returns complex data to the cloud layer, wherein the data comprises first sampling frequency data and second sampling frequency data which are stored in the first storage module and the second storage module and collected under the first sampling frequency and the second sampling frequency. The first data storage module transmits the first sampling frequency data through the network start node S1, and the second data storage module transmits the second sampling frequency data through the network start node S2.
Preferably, the first and second sampling frequency data, in which the first and second time series data are consecutive, may be transmitted as one independent data packet.
And 5.3, calculating a first shortest link path and a second shortest link path which are formed by available unoccupied links (shown by solid lines) except occupied links (shown by dotted lines) in the network of the nodes of the transport layer network by respectively taking S1 and S2 as starting points and d1 and d2 as end points according to the first sampling frequency data and the second sampling frequency data. And the first shortest link path and the second shortest link path are not the link paths occupying the least transit nodes i.
And 5.4, determining the actual transmission link paths of the first sampling frequency data and the second sampling frequency data.
Based on the first and second shortest link paths determined in step 5.3, when a node (ir) where the first and second shortest link paths overlap occurs, it is determined whether a time collision occurs when the first and second sampling frequency data pass through the overlapping node ir according to the data amount and the bandwidth of the first and second sampling frequency data. The method comprises the following steps:
1) determining the link path selection priority of the first sampling frequency data and the second sampling frequency data specifically comprises the following steps: comparing the minimum value (i.e. earlier time) of the first time sequence data and the second time sequence data contained in the first and second sampling frequency data, one of the first and second sampling frequency data having the smaller minimum value of the time sequence data has higher link path selection priority.
2) The determination condition of the time conflict may be set such that a time difference between a first time at which a higher link path selection priority side of the first and second sampling frequency data is expected to be transmitted from the overlapping node and a second time at which a lower link path selection priority side of the first and second sampling frequency data is expected to be transmitted to the overlapping node is greater than a specific threshold, and the specific threshold may be an average value of transmission times of the first and second sampling frequency data between any two transmission nodes according to a current bandwidth condition.
3) If no time conflict occurs, directly taking the first shortest link path and the second shortest link path in the step 5.3 as actual transmission link paths of the first sampling frequency data and the second sampling frequency data; if time conflict occurs, the actual transmission link path of the higher link path selection priority side in the first and second sampling frequency data is set as the shortest link path determined in step 5.3, and data transmission is performed. For the party with lower link path selection priority in the first and second sampling frequency data, which has a larger minimum value of timing data, adjusting its actual transmission link path, specifically includes:
1) all unoccupied parent nodes if of the above-mentioned overlapping nodes are queried and all alternative paths downstream of the unoccupied parent nodes if not via the above-mentioned overlapping nodes (ir) are calculated.
2) And calculating the transmission time required by all the alternative paths according to the data volume and the bandwidth condition of the side with lower link path selection priority in the first and second sampling frequency data.
3) And determining a final adjustment path according to the transmission time and the transit node data of the alternative path. The method specifically comprises the following steps: the method comprises the steps of screening out alternative paths with transmission time longer than that of a transmission link path of one party with higher link path selection priority in first sampling frequency data and second sampling frequency data, and then further selecting a link path occupying the minimum transit node i from the selected alternative paths as a final adjustment path.
And 5.5, transmitting the first sampling frequency data and the second sampling frequency data to a cloud storage module according to the actual transmission link paths of the first sampling frequency data and the second sampling frequency data determined in the step 5.4, and integrating. The method specifically comprises the following steps:
and transmitting the first and second sampling frequency data to a cloud storage module, wherein the cloud storage module comprises a cache part and a memory part. The first and second sampling frequency data are firstly stored in a cache part, and after the cache part carries out ascending sequencing on the first time sequence data and the second time sequence data contained in each section of the first and second sampling frequency data, the first time sequence data and the second time sequence data and the sequence of the sequenced first and second sampling frequency detection data are stored for subsequent calculation and analysis.
The method and the equipment configuration for processing the edge calculation data of the intelligent instrument have the following beneficial effects: the detection state of the sensor can be adjusted according to the whole network state of the pipe, and the data in different detection states are grouped, processed, transmitted and integrated, so that the scientificity and the accuracy of the intelligent instrument of the urban gas pipe network for monitoring the network state are guaranteed under the conditions that the overall efficiency of the edge computing equipment is higher and the occupied resources are lower. The method specifically comprises the following steps:
1) by reasonably and scientifically setting the mathematical range of the floating threshold values of the absolute values of the temperature and the flow and the sudden change threshold value of the slope change rate of the pressure curve, the sensitivity of the intelligent instrument in the edge computing system to the fluctuation of the sensitive parameters of the pipe network can be improved, and the problem that the intelligent instrument cannot respond in time when the sensitive parameters generate slight fluctuation is solved.
2) When the sensitive parameters of the pipe network meet the threshold conditions, the sampling frequency of the pipe network parameters is improved, the problems that instantaneous parameter fluctuation is not easy to find and characteristic data under the condition of specific parameter change is omitted due to the fact that the sampling frequency of the intelligent instrument is low in normal state monitoring are solved, and the accuracy of monitoring the subsequent safety or other states of the gas pipe network is improved.
3) The first sampling frequency data and the second sampling frequency data are stored in a classified mode and are transmitted respectively, and the data are transmitted to the cloud storage module earlier through the transmission layer link path design, the fact that the data with lower time sequence data (detected earlier) can be transmitted to the cache of the cloud storage module earlier is guaranteed, the early data receiving and caching of the data with the earlier detection are avoided, large-scale data sequence rearrangement adjustment is avoided, occupation of system resources in the cloud storage module during data sequencing and rearrangement is reduced, the overall efficiency of edge computing equipment is higher, and under the condition that the occupied resources are lower, the scientificity and accuracy of the urban gas pipe network intelligent instrument for monitoring the state of the pipe network are guaranteed.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A data processing method for edge calculation of an intelligent instrument is characterized by comprising the following steps: the method comprises the following steps:
step 1, initializing equipment, and carrying out conventional detection on an intelligent instrument, wherein the sampling frequency of the intelligent instrument in the conventional detection process is a first sampling frequency;
step 2, judging whether at least one of the intelligent meters detected aiming at the plurality of parameters reaches a first threshold condition; if yes, entering step 3, if not, continuing step 2;
step 3, setting the sampling frequency detected by the intelligent instrument as a second sampling frequency;
and 4, respectively storing and transmitting the first sampling frequency data and the second sampling frequency data, and integrating the first sampling frequency data and the second sampling frequency data at a cloud layer.
2. The method for processing the edge calculation data of the intelligent instrument according to claim 1, wherein the method comprises the following steps: wherein the second sampling frequency is greater than the first sampling frequency.
3. The method for processing the edge calculation data of the intelligent instrument according to claim 2, wherein the method comprises the following steps: the second sampling frequency is 10-30 times the first sampling frequency.
4. The method for processing the edge calculation data of the intelligent instrument according to claim 1, wherein the method comprises the following steps: the first sampling frequency data includes first sampling frequency detection data of the sensor and first timing data corresponding to the first sampling frequency detection data.
5. The method for processing the edge calculation data of the intelligent instrument according to claim 1, wherein the method comprises the following steps: the second sampling frequency data includes second sampling frequency detection data of the sensor and second timing data corresponding to the second sampling frequency detection data.
6. The data processing method for the edge calculation of the intelligent instrument is characterized in that: the first time-series data and the second time-series data are calibrated in chronological order of actual detection.
7. The data processing method for the edge calculation of the intelligent instrument is characterized in that: the first and second time sequence data in the first and second sampling frequency data are continuous first and second sampling frequency data and are sent as an independent data packet.
8. The method for processing the edge calculation data of the intelligent instrument according to claim 1, wherein the method comprises the following steps: the method further comprises the following steps: determining whether all of the intelligent meters detected for the plurality of parameters meet a second threshold condition; if yes, go to step 1, if no, continue to step 3.
9. The method for processing the edge calculation data of the intelligent instrument according to claim 1, wherein the method comprises the following steps: when the cloud layer needs to read the measurement data, the data of the data storage module is sent to the cloud storage module of the cloud layer.
10. An intelligent instrument edge computing device configuration for a city gas pipe network, which is used for executing the intelligent instrument edge computing data processing method of one of claims 1 to 9, and is characterized in that: the system comprises a detection layer, an edge layer, a transmission layer and a cloud end layer which are connected in sequence through a communication means.
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| US20180144621A1 (en) * | 2016-11-21 | 2018-05-24 | Nec Corporation | Measurement data processing method |
| CN108343844A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | A kind of Multi-parameter modularized oil-gas pipeline safety monitoring system and method |
| CN110163523A (en) * | 2019-05-29 | 2019-08-23 | 陈述 | A kind of resident's power consumption characteristics statistical method and system based on edge calculations |
| CN110708370A (en) * | 2019-09-27 | 2020-01-17 | 中移物联网有限公司 | Data processing method and terminal |
| CN111623869A (en) * | 2020-05-20 | 2020-09-04 | 北京必创科技股份有限公司 | Data processing method based on edge calculation and data monitoring and edge calculation device |
| CN113255584A (en) * | 2021-06-22 | 2021-08-13 | 德明通讯（上海）股份有限公司 | Fault diagnosis and monitoring system based on edge calculation |
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