CN113709599B - Edge calculation data processing method and equipment configuration for intelligent instrument - Google Patents
Edge calculation data processing method and equipment configuration for intelligent instrument Download PDFInfo
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Abstract
The application 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 performing conventional detection by 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 for detecting a plurality of parameters reaches a first threshold condition; if yes, entering a step 3, and if not, continuing the 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 in a cloud layer.
Description
Technical Field
The application relates to a data processing method, in particular to an edge calculation data processing method and equipment configuration for an intelligent instrument.
Background
The urban gas pipe network has the characteristics of wide regional distribution, multiple equipment types, various network connection, variable operation modes and the like. With 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 multi-element data are exponentially increased, and the data quantity reaches a large data level. Traditional cloud computing is gradually restricted in application in urban gas pipe networks due to data transmission limitation.
In recent years, edge computing is recently paid attention to, edge computing refers to a novel computing model for performing computing on network edges, objects of edge computing operations include downstream data from cloud services and upstream data from internet of everything services, and edges of edge computing refer to any computing and network resources between paths from data sources to cloud computing centers. The edge calculation is a distributed open platform integrating network, calculation, storage and application core capabilities at the network edge side close to an object or data source, provides edge intelligent service nearby, and meets key requirements of industry digitization in aspects of agility connection, real-time service, data optimization, application intelligence, safety, privacy protection and the like. It can be used as a bridge connecting physical and digital worlds, enabling intelligent assets, intelligent gateways, intelligent systems and intelligent services.
However, in the prior art, the detection end of edge calculation is usually in a state of long-term monitoring, so the sampling frequency is usually set to a conventional detection frequency with a lower frequency, and the instantaneous parameter fluctuation is not easy to be found on one side of the lower frequency, and on the other side, characteristic data under the condition of specific parameter change is easy to be missed. For gas pipe networks, instantaneous parameter fluctuations or, in particular, peak data are of great importance for their state analysis. The omission of the data can cause the reduction of the safety of the subsequent gas pipe network or the monitoring accuracy of other states.
Therefore, it is necessary to provide a method and a device configuration for processing edge calculation data of an intelligent instrument, which can adjust the detection state of a sensor according to the state of the whole pipe network, and perform grouping processing, transmission and integration according to the data of different detection states, so that the scientificity and the accuracy of monitoring the state of the pipe network for the intelligent instrument of the urban gas pipe network are ensured under the condition that the overall efficiency of the edge calculation device is higher and the occupied resources are lower.
Disclosure of Invention
The technical problems to be solved by the application are the following defects in the prior art: in the prior art, the detection end of edge calculation is usually in a state of long-term monitoring, so the sampling frequency is usually set to be a conventional detection frequency with a lower frequency, and the instantaneous parameter fluctuation is not easy to find on one hand, and on the other hand, characteristic data under the condition of specific parameter change is easy to miss. For gas pipe networks, instantaneous parameter fluctuations or, in particular, peak data are of great importance for their state analysis. The omission of the data can cause the reduction of the safety of the subsequent gas pipe network or the monitoring accuracy of other states.
The technical scheme adopted for solving the technical problems is as follows:
a method for intelligent meter edge computing data processing, the method comprising:
step 1, initializing equipment, and performing conventional detection by 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 for detecting a plurality of parameters reaches a first threshold condition; if yes, entering a step 3, and if not, continuing the 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 in a cloud layer.
Specifically, the second sampling frequency is greater than the first sampling frequency.
In particular, 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 timing 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 timing data corresponding to the second sampling frequency detection data.
Specifically, the first time sequence data and the second time sequence data are calibrated according to the time sequence of actual detection.
Specifically, 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.
Specifically, the method further comprises the steps of: judging whether all intelligent meters detected for a plurality of parameters reach a second threshold condition; if yes, go to step 1, if no, continue 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 of the intelligent instrument edge computing equipment for the urban gas pipe network is used for executing the intelligent instrument edge computing data processing method, and a detection layer, an edge layer, a transmission layer and a cloud layer are connected in sequence through a communication means.
The method and the equipment configuration for processing the intelligent instrument edge calculation data have the following beneficial effects: the detection state of the sensor can be adjusted aiming at the whole pipe network state, and grouping processing, transmission and integration are carried out aiming at data of different detection states, so that the scientificity and the accuracy of the intelligent instrument of the urban gas pipe network for monitoring the pipe network state are ensured under the conditions that the overall efficiency of the edge computing equipment is higher and the occupied resources are lower. The method comprises the following steps:
1) By setting reasonable and scientific mathematical ranges aiming at the floating threshold value of the absolute value of the temperature and the flow and the abrupt 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 slightly fluctuate is avoided.
2) When the pipe network sensitive parameters meet the threshold condition, 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 low sampling frequency of the intelligent instrument in normal monitoring are solved, and the accuracy of monitoring the follow-up gas pipe network safety or other states is improved.
3) The first sampling frequency data and the second sampling frequency data are classified and stored and respectively transmitted, and the data with lower time sequence data (detected earlier) can be transmitted to the cache of the cloud storage module earlier through the design of the transmission layer link path, and the detection of the earlier data is accepted and cached earlier, so that the large-scale data sequence rearrangement adjustment is avoided, the occupation of system resources in the cloud storage module when the data are ordered and rearranged is reduced, the overall efficiency of the edge computing equipment is higher, and the scientificity and the accuracy of network management state monitoring of the intelligent instrument for the urban gas network are guaranteed under the condition that the occupation resources are lower.
Drawings
FIG. 1 is a schematic diagram of the edge computing system for an intelligent instrument of an urban gas pipe network.
Fig. 2 is a node structure of a transmission layer of an apparatus configuration according to the present application.
Detailed Description
The present application will be described in more detail below with reference to the attached drawings, in which preferred embodiments of the present application are shown, it being understood that one skilled in the art can modify the present application described herein while still achieving the beneficial effects of the present application. Accordingly, the following description is to be construed as broadly known to those skilled in the art and not as limiting the application.
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 application in unnecessary detail. It will be appreciated that in the development of any such actual embodiment, numerous implementation details must be made in order to achieve the developer's specific goals.
In order to make the objects and features of the present application more comprehensible, embodiments accompanied with figures are described in detail below. It should be noted that the drawings are in a very simplified form and all employ non-precise ratios, and are merely convenient and clear to aid in the description of the embodiments of the application.
The application provides a method and equipment configuration for processing intelligent instrument edge calculation data.
The equipment configuration comprises a detection layer, an edge layer, a transmission layer and a cloud layer which are sequentially connected through 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, … …, and 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 real-time data of each of the plurality of sensors is stored in correspondence with 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 adjustment 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 the data transmitted by the detection layer, and comprises a plurality of storage nodes which are 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 acquired 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 when the execution conditions are reached, the data processing module is further used for controlling the execution module to send specific driving control signals to downstream components controlled by the execution module. The downstream components include respective 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 adjustable sampling frequency can be obtained under specific conditions.
The storage nodes of the data storage module upload data to the cloud layer through the transmission layer, so that the cloud computing device can calculate and analyze the data of the storage nodes of the data storage module. Specifically, different paths of data in the network node through the transport layer are set according to the storage position (i.e. the storage in the first storage module or the second storage module) of the data in the storage node 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 computing and analyzing storage node data of the data storage module through the cloud computing device.
The transmission layer is a transmission network comprising 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 method for processing the intelligent instrument edge calculation data based on the equipment configuration, which comprises the following steps:
step one, initializing a system, setting the sampling frequency of a sampling frequency adjusting module to be a first sampling frequency f1, and collecting first sampling frequency data of each of a plurality of sensors through a data collecting module of an edge layer 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 series data can correspond to the corresponding first sampling frequency detection data, and can represent time points of detection of a plurality of first sampling frequency detection data.
Wherein the sensor comprises: temperature sensors, gas pressure sensors, gas flow sensors, etc. The sensor may be installed at the inlet/outlet of the voltage regulating station, the main line of the cell pipe network, the branch line of the cell pipe network, preferably at the customer end of the branch line of the cell 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 reach the condition of starting the second sampling frequency. If the condition of starting the second sampling frequency is reached, the execution module sends a driving control signal to the sampling frequency adjustment module, adjusts the sampling frequency of the plurality of sensors to the second sampling frequency, wherein the second sampling frequency f2 is larger than the first sampling frequency f1, specifically, the second sampling frequency f2 is n times the first sampling frequency f1, wherein n is an integer of 10-30, preferably n is an integer of 10-20, and executes the step three. If the condition of starting the second sampling frequency is not reached, the condition of the first sampling frequency is maintained, and the real-time detection of the second step is continuously executed.
Specifically, initiating the second sampling frequency condition includes: at least one of the following three conditions is satisfied: 1) Real-time temperature detected by the temperature sensor meetsWherein Ti is temperature data acquired in real time, i is the ith time point, and +.>Is the average value of the 1 st to i th temperature data, T j All data acquired at 1 st to i th time acquisition points are represented; and/or 2) the real-time detection data of the pressure sensor satisfies +.>Wherein P (t) i ) The system is characterized in that the system is air pressure data acquired in real time, wherein ρ is natural gas density, and g is gravity acceleration; and/or 3) the flow sensor real-time detection data satisfiesLi is flow data acquired in real time, i is the ith time point,/and Li is the time point>Is the average value of the 1 st to i th flow data, L j All flow data acquired at 1 st to i th time acquisition points are represented. When at least one of the above conditions 1) -3) is satisfied, it is determined that the start-up second sampling frequency condition is reached.
And thirdly, acquiring data of the n sensors by adopting a second sampling frequency, and acquiring the second sampling frequency data of each of the plurality of sensors by a data acquisition module of the edge layer and storing the second sampling frequency data in a second storage module of a 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 sequence data and the second time sequence data are calibrated according to the time sequence of actual detection.
And step four, detecting the second sampling frequency condition, judging that the second sampling frequency condition is out when the second sampling frequency condition is met, executing the step one, and if the second sampling frequency condition is not out, continuing to execute the step three.
The exit second sampling frequency condition is that the following three conditions are satisfied simultaneously: 1) Real-time temperature detected by the temperature sensor meetsWherein Ti is temperature data acquired in real time, i is the ith time point, and +.>Is the average value of the 1 st to i th temperature data, T j All data acquired at 1 st to i th time acquisition points are represented; and 2) the real-time detection data of the pressure sensor satisfies +.>Wherein P (t) i ) The system is characterized in that the system is air pressure data acquired in real time, wherein ρ is natural gas density, and g is gravity acceleration; and 3) the real-time detection data of the flow sensor satisfies +.>Li is flow data acquired in real time, i is the ith time point,/and Li is the time point>Is the average value of the 1 st to i th flow data, L j All flow data acquired at 1 st to i th time acquisition points are represented. When the three conditions are satisfied at the same time, it is determined that the exit second sampling frequency condition is satisfied.
And fifthly, when the cloud layer needs to read the measurement data, sending the data of the data storage module to the cloud storage module of the cloud layer. Referring to fig. 2, specifically, the method includes:
step 5.1, when the cloud layer needs to read the measurement data, the cloud transmits a data request to a specific edge node of the data storage module;
and 5.2, the edge node receiving the data request replies data to the cloud layer, wherein the data comprises first sampling frequency data and second sampling frequency data acquired under the first sampling frequency and the second sampling frequency stored in the first storage module and the second storage module. The first data storage module transmits the first sampling frequency data transmission through the network initiation node S1, and the second data storage module transmits the second sampling frequency data transmission through the network initiation node S2.
Preferably, the first and second sampling frequency data, in which the first and second time series data are continuous, may be transmitted as one independent data packet.
And 5.3, for the first and second sampling frequency data, respectively taking S1 and S2 as starting points and d1 and d2 as end points, calculating first and second shortest link paths formed by unoccupied available links (shown by solid lines) except occupied links (shown by dotted lines) in the network node network of the transmission layer network. The first and second shortest link paths are not the link paths occupying the least of the transfer nodes i.
And 5.4, determining the actual transmission link paths of the first and second sampling frequency data.
And (3) judging whether time conflict occurs when the first and second shortest link paths pass through the overlapped node ir according to the data quantity and the bandwidth of the first and second sampling frequency data when the first and second shortest link paths are overlapped. Comprising 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) of the first time series data and the second time series data contained in the first and second sampling frequency data, one of the first and second sampling frequency data having a smaller minimum value of the time series data has a higher link path selection priority.
2) And judging whether time conflict occurs when the first and second sampling frequency data passes through the overlapped node ir, wherein the judging condition of the time conflict can be set as that the time difference between the first time when a higher link path selection priority party in the first and second sampling frequency data is expected to be transmitted from the overlapped node and the second time when a lower link path selection priority party in the first and second sampling frequency data is expected to be transmitted to the overlapped node is larger than a specific threshold value, and the specific threshold value can be an average value of the transmission time of the first and second sampling frequency data between any two transmission nodes according to the current bandwidth condition.
3) If no time conflict occurs, the first and second shortest link paths in the step 5.3 are directly used as actual transmission link paths of the first and second sampling frequency data; if time conflict occurs, the actual transmission link path of the party with higher link path selection priority in the first and second sampling frequency data is set as the shortest link path determined in the step 5.3, and data transmission is carried out. For the party with lower link path selection priority in the first and second sampling frequency data, which has larger time sequence data minimum value, the actual transmission link path is adjusted, which comprises the following steps:
1) Querying all unoccupied parent nodes if of the overlapping nodes and calculating all alternative paths of nodes (ir) downstream of the unoccupied parent nodes if not passing through the overlapping nodes.
2) And calculating the transmission time required by all the alternative paths according to the data quantity and the bandwidth condition of the side with the 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: firstly, screening out an alternative path with transmission time longer than that of a transmission link path of a party with higher link path selection priority in the first sampling frequency data and the second sampling frequency data, and then further selecting a link path occupying the least 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 first sampling frequency data and the second sampling frequency data. The method specifically comprises the following steps:
and transmitting the first sampling frequency data and the second sampling frequency data to a cloud storage module, wherein the cloud storage module comprises a cache part and a memory part. The first sampling frequency data and the second sampling frequency data are firstly stored in a buffer memory part, after the first time sequence data and the second time sequence data contained in each section of the first sampling frequency data and the second sampling frequency data are sequenced in an ascending order, the first time sequence data, the second time sequence data and the corresponding first sampling frequency detection data and the second sampling frequency detection data are sequenced in order, and then follow-up calculation analysis is carried out.
The method and the equipment configuration for processing the intelligent instrument edge calculation data provided by the application have the following beneficial effects: the detection state of the sensor can be adjusted aiming at the whole pipe network state, and grouping processing, transmission and integration are carried out aiming at data of different detection states, so that the scientificity and the accuracy of the intelligent instrument of the urban gas pipe network for monitoring the pipe network state are ensured under the conditions that the overall efficiency of the edge computing equipment is higher and the occupied resources are lower. The method comprises the following steps:
1) By setting reasonable and scientific mathematical ranges aiming at the floating threshold value of the absolute value of the temperature and the flow and the abrupt 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 slightly fluctuate is avoided.
2) When the pipe network sensitive parameters meet the threshold condition, 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 low sampling frequency of the intelligent instrument in normal monitoring are solved, and the accuracy of monitoring the follow-up gas pipe network safety or other states is improved.
3) The first sampling frequency data and the second sampling frequency data are classified and stored and respectively transmitted, and the data with lower time sequence data (detected earlier) can be transmitted to the cache of the cloud storage module earlier through the design of the transmission layer link path, and the detection of the earlier data is accepted and cached earlier, so that the large-scale data sequence rearrangement adjustment is avoided, the occupation of system resources in the cloud storage module when the data are ordered and rearranged is reduced, the overall efficiency of the edge computing equipment is higher, and the scientificity and the accuracy of network management state monitoring of the intelligent instrument for the urban gas network are guaranteed under the condition that the occupation resources are lower.
The foregoing has shown and described the basic principles, principal features and advantages of the application. It will be understood by those skilled in the art that the present application is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present application, and various changes and modifications may be made without departing from the spirit and scope of the application, which is defined in the appended claims. The scope of the application is defined by the appended claims and equivalents thereof.
Claims (8)
1. The edge calculation data processing method for the intelligent instrument is characterized by comprising the following steps of: the method is based on an equipment configuration comprising: the detection layer, the edge layer, the transmission layer and the cloud layer are connected in sequence through communication means; the detection layer comprises a plurality of sensors, including a 1 st sensor, a 2 nd sensor, a … … and an n-th sensor; each of the plurality of sensors is connected to the edge layer through a network, and real-time data of each of the plurality of sensors is correspondingly 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 adjustment 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; the data storage module is used for storing the data transmitted by the detection layer and comprises a plurality of storage nodes which are 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 acquired 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 controlling the execution module to send specific driving control signals to downstream components controlled by the execution module when the execution conditions are reached; the downstream component comprises various sensors of the detection layer and a sampling frequency adjusting 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 sensor acquisition data with adjustable sampling frequency can be obtained under specific conditions; the storage nodes of the data storage module upload data to the cloud layer through the transmission layer, so that the cloud computing device can calculate and analyze the data of the storage nodes of the data storage module; setting different paths of data in a network node in a transmission layer according to the data storage position in a storage node of a data storage module; the cloud layer comprises a cloud storage module and a cloud computing module, and is used for receiving data of the data storage module through the transmission layer and computing and analyzing storage node data of the data storage module through the cloud computing device; the transmission layer is a transmission network comprising N nodes, and different transmission node paths are selected in the transmission layer according to different storage module positions and network conditions;
the edge calculation data processing method for the intelligent instrument comprises the following steps:
step 1, initializing equipment, and performing conventional detection by 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 for detecting a plurality of parameters reaches a first threshold condition; if yes, entering a step 3, and if not, continuing the step 2;
step 3, setting the sampling frequency detected by the intelligent instrument as a second sampling frequency;
step 4, storing and transmitting the first sampling frequency data and the second sampling frequency data respectively, and integrating the first sampling frequency data and the second sampling frequency data in a cloud layer;
step 5, when the cloud layer needs to read the measurement data, sending the data of the data storage module to the cloud storage module of the cloud layer; the method specifically comprises the following steps:
step 5.1, when the cloud layer needs to read the measurement data, the cloud transmits a data request to a specific edge node of the data storage module;
step 5.2, the edge node receiving the data request replies data to the cloud layer, wherein the data comprises first sampling frequency data and second sampling frequency data acquired under a first sampling frequency and a second sampling frequency stored in a first storage module and a second storage module; the first data storage module sends the first sampling frequency data through the network starting node S1, and the second data storage module sends the second sampling frequency data through the network starting node S2;
step 5.3, for the first and second sampling frequency data, respectively taking the network nodes S1 and S2 as starting points and taking the network nodes d1 and d2 as end points, calculating a first and second shortest link paths formed by unoccupied available links except occupied links in the transmission layer network node network; the first and second shortest link paths are link paths occupying the least of the transfer nodes i;
step 5.4, determining the actual transmission link paths of the first and second sampling frequency data, specifically:
based on the first and second shortest link paths determined in step 5.3, when the first and second shortest link paths have overlapping nodes, determining whether a time conflict occurs when the first and second sampling frequency data passes through the overlapping nodes according to the data amounts and bandwidths of the first and second sampling frequency data, and further comprising:
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 of the first time sequence data and the second time sequence data contained in the first sampling frequency data and the second sampling frequency data, wherein one of the first sampling frequency data and the second sampling frequency data with smaller time sequence data minimum value has higher link path selection priority;
2) Judging whether time conflict occurs when the first sampling frequency data and the second sampling frequency data pass through the overlapped node, wherein the judging condition of the time conflict is set as that the time difference between the first time when the first sampling frequency data and the second time when the second sampling frequency data are expected to be transmitted to the overlapped node by the party with higher link path selection priority in the first sampling frequency data and the second time when the first sampling frequency data and the second sampling frequency data are expected to be transmitted by the party with lower link path selection priority in the first sampling frequency data and the second sampling frequency data is larger than a specific threshold value, and the specific threshold value is the average value of the transmission time of the first sampling frequency data and the second sampling frequency data between any two transmission nodes according to the current bandwidth condition;
3) If no time conflict occurs, the first and second shortest link paths in the step 5.3 are directly used as actual transmission link paths of the first and second sampling frequency data; if the time conflict occurs, setting the actual transmission link path of the party with higher link path selection priority in the first and second sampling frequency data as the shortest link path determined in the step 5.3, and carrying out data transmission; for the party with lower link path selection priority in the first and second sampling frequency data, which has larger time sequence data minimum value, the actual transmission link path is adjusted, which comprises the following steps:
1) Querying all unoccupied parent nodes if of the overlapped nodes, and calculating all alternative paths of the nodes downstream of the unoccupied parent nodes if and not passing through the overlapped nodes;
2) Calculating the transmission time required by all the alternative paths according to the data quantity and the bandwidth condition of one party with lower link path selection priority in the first and second sampling frequency data;
3) 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: firstly screening out an alternative path with transmission time longer than that of a transmission link path of a party with higher link path selection priority in the first sampling frequency data and the second sampling frequency data, and then further selecting a link path occupying the least transit node i from the selected alternative paths as a final adjustment path;
step 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 first sampling frequency data and the second sampling frequency data; the method specifically comprises the following steps: transmitting the first sampling frequency data and the second sampling frequency data to a cloud storage module, wherein the cloud storage module comprises a cache part and a memory part; the first sampling frequency data and the second sampling frequency data are firstly stored in a buffer memory part, after the first time sequence data and the second time sequence data contained in each section of the first sampling frequency data and the second sampling frequency data are sequenced in an ascending order, the first time sequence data, the second time sequence data and the corresponding first sampling frequency detection data and the second sampling frequency detection data are sequenced in order, and then follow-up calculation analysis is carried out.
2. The method for intelligent meter edge calculation data processing according to claim 1, wherein: wherein the second sampling frequency is greater than the first sampling frequency.
3. The method for intelligent meter edge calculation data processing according to claim 2, wherein: the second sampling frequency is 10-30 times the first sampling frequency.
4. The method for intelligent meter edge calculation data processing according to claim 1, wherein: 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 intelligent meter edge calculation data processing according to claim 1, wherein: 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 method for intelligent meter edge calculation data processing according to claim 4 or 5, wherein: the first time sequence data and the second time sequence data are calibrated according to the time sequence of actual detection.
7. The method for intelligent meter edge calculation data processing according to claim 1, wherein: the method further comprises the steps of: judging whether all intelligent meters detected for a plurality of parameters reach a second threshold condition; if yes, go to step 1, if no, continue step 3.
8. The method for intelligent meter edge calculation data processing according to claim 1, wherein: when the cloud layer needs to read the measurement data, the data of the data storage module are sent to the cloud storage module of the cloud layer.
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