CN110020752B - Data acquisition optimization method, device, equipment and storage medium of multi-parameter monitoring device - Google Patents
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Abstract
The invention discloses a data acquisition optimization method, a data acquisition optimization device, data acquisition optimization equipment and a storage medium of a multi-parameter monitoring device, and belongs to the field of online monitoring of power transmission lines. The invention provides a calculation method for simultaneously ensuring the service life requirement of the monitoring device and the maximum interval requirement of data acquisition for the minimum battery capacity from the aspect of probability statistical distribution, and the calculation method has low complexity and strong practicability in the engineering field; a data acquisition interval optimization strategy is provided for the actual battery capacity, so that the real-time performance of data monitoring is further guaranteed, and time slot conflicts of different parameter acquisition can be reduced; in order to deal with the monitoring of abnormal parameters, the invention also provides a time slot allocation strategy of the processor, which respectively carries out different processing aiming at the conventional conditions and the abnormal data conditions, ensures the continuous tracking feedback of the abnormal parameters, and simultaneously provides a sensor priority setting method which gives consideration to fairness and improves the instantaneity of abnormal data monitoring, thereby being easy to be applied in engineering.
Description
Technical Field
The invention relates to the technical field of on-line monitoring of power transmission lines, in particular to a data acquisition optimization method, a data acquisition optimization device, data acquisition optimization equipment and a storage medium of a multi-parameter monitoring sensor.
Background
China is wide in territory, the scale of a power grid is extremely large, and a power transmission line is millions of kilometers long. Most of the components of the power transmission line are directly exposed to the natural environment, so that the power transmission line is inevitably affected by various natural environments and other factors, including: the action of mechanical force such as the dead weight of a line, wind power, ice and snow, the erosion of harmful gas in the air, the change of temperature and the like. Under the action of various stresses, the power transmission line is very easy to break down to cause large-scale power failure, so that huge loss is caused to national economy, real-time monitoring on the power transmission line is very important, early warning is timely carried out on the fault condition of the power transmission line, and measures are taken to avoid damage caused by sudden faults.
With the development of information technology, sensor technology is gaining more and more attention in the power grid. Currently, a wireless sensor network composed of sensors with different functions plays an important role in monitoring and fault feedback of a power grid. For the state monitoring of the power transmission line, a sensor with a multi-parameter monitoring function is generally adopted, and one set of equipment is used for monitoring various states of the power transmission line. However, due to the limitation of the application environment, the multi-parameter monitoring device for the power transmission line must meet the basic requirements of low power consumption, small size, light weight and low cost. Therefore, the multi-parameter monitoring sensor can only be provided with a single-core processor generally, the battery capacity is small, and the battery energy must be supplemented through a solar panel so as to meet the long-term reliable operation of the monitoring device. Because the solar energy collection amount has larger randomness in time and space, and sudden abnormal events also exist in the monitoring process, the exact theoretical battery capacity meeting the expected service life of the monitoring device cannot be determined. The current battery capacity setting in engineering is judged according to cost and experience, the battery configuration is fixed and unchanged, and the flexible strain capacity to different environments needs to be improved. And the data acquisition interval then sets up fixed numerical value when putting in according to monitoring devices earlier stage, does not consider prime factor, probably can arouse a large amount of time slot conflicts, in addition, because the acquisition interval is fixed unchangeable, lack the self-adaptation adjustment to different situation, when unusual proruption needs in time to report, if this acquisition interval overlength, can cause unusual report to respond slowly, if this acquisition time is too short, then the energy waste that can cause when normal condition. Therefore, the need to ensure data monitoring performance and monitor device lifetime, battery capacity configuration, data acquisition interval setting, and reasonable processor resource allocation, are engineering issues that must be addressed before deployment of such devices.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the technical problems, the invention provides a data acquisition optimization method of a multi-parameter monitoring device, which meets the requirement of the maximum acquisition interval of various data while guaranteeing the service life of the monitoring device, and reduces the cost of the device.
It is a further object of the present invention to provide a data acquisition optimization device, an apparatus and a computer readable storage medium for a corresponding multi-parameter monitoring device.
The technical scheme is as follows: according to a first aspect of the present invention, there is provided a method for optimizing data acquisition of a multi-parameter monitoring device, the method comprising the steps of:
obtaining the minimum battery capacity required by meeting the basic performance requirement of the device by using the deployment parameters and the historical statistical data, and configuring the actual battery capacity of the monitoring device based on the minimum battery capacity;
calculating a theoretical minimum value of the cycle time of primary data acquisition, and solving and setting an actual data acquisition interval according to a number theory based on a real-time requirement of monitoring and a service life requirement of a monitoring device; and
and allocating the acquisition time slots of the monitoring devices in a time division multiplexing mode.
Further, the obtaining the minimum battery capacity required to meet the basic performance requirement of the device by using the deployment parameters and the historical statistical data comprises the following steps:
calculating the abnormal probability of various parameter data according to historical data, and estimating the electric energy consumption of the monitoring device in one day based on the abnormal probability and the maximum acquisition interval requirement of various data;
obtaining the energy deficit of the monitoring device according to the solar energy collected by the monitoring device within one day and the estimated electric energy consumption;
the service life requirement of the monitoring device is converted into a relational expression of the energy deficit and the minimum battery capacity, and a calculation formula of the minimum battery capacity is obtained through mathematical conversion, so that the minimum battery capacity is obtained.
Further, the actual acquisition interval setting method of the data is as follows:
respectively calculating theoretical maximum value T of cycle duration of one-round multi-parameter data acquisitionmaxAnd a minimum value Tmin：
In the formula
The maximum collection interval of the data of the ith parameter]Representing the least common multiple, N representing the number of sensors, ciEnergy consumed for acquisition and processing of the ith parameter, piFor the probability of abnormality of the ith parameter data in the history statistics, τiAcquisition interval for ith parameter data in one data acquisition cycleC is a shortening ratio ofrealThe energy consumption of the monitoring device in one day, and delta t is the unit time slot length of a CPU of the monitoring device;
order to
The following constraint relationship is obtained for the data acquisition interval of the ith parameter to be set actually according to the real-time performance of data monitoring and the service life requirement of the monitoring device by combining a number theory:
solving the above formula by greedy algorithm to obtain
Further, the allocating the collection time slot of the monitoring device in the time division multiplexing manner includes: and performing modulus operation according to the set actual data acquisition interval and the current acquisition time, and distributing the data acquired by the sensor when the modulus operation result of one sensor is zero and no time slot conflict exists.
Further, the method further comprises: preprocessing the acquired data, and increasing the priority of the sensor corresponding to the abnormal parameters and shortening the data acquisition interval when the data are found to be abnormal.
Further, the method further comprises: when the data acquisition time slots of the sensors conflict, the variable weight calculator and the priority level are set to cooperate to schedule data acquisition, and the specific method is as follows:
a weight counter is attached to each priority, and the priorities (i, j) and the weights (w) of the sensors are comparedi,wj) If i > j and i + wi≥j+wjIf so, increasing the weight variation delta omega of the sensor weight with low priority, and decreasing the weight variation delta omega of the sensor weight with high priority; if i > j and i + wi＜j+wjThen the priorities of the two sensors are exchanged and the weight w is weightediAnd wjReset to 0.
According to a second aspect of the present invention, there is provided a data acquisition optimization device for a multi-parameter monitoring device, the device comprising:
the minimum battery capacity calculation module is used for obtaining the minimum battery capacity required by meeting the basic performance requirement of the monitoring device according to the deployment parameters and the historical statistical data;
the acquisition interval setting module is used for calculating the theoretical minimum value of the cycle time of primary data acquisition, and solving the actual acquisition interval of the data according to a number theory based on the real-time requirement of monitoring and the service life requirement of the monitoring device;
and the time slot distribution module is used for distributing the acquisition time slots of the monitoring device in a time division multiplexing mode.
Further, the minimum battery capacity calculation module includes:
the electric energy consumption estimation unit is used for calculating the abnormal probability of various parameter data according to historical data and estimating the electric energy consumption of the monitoring device in one day based on the abnormal probability and the maximum acquisition interval requirement of various data;
the energy deficit calculation unit is used for obtaining the energy deficit of the monitoring device according to the solar energy collected by the monitoring device within one day and the estimated electric energy consumption;
minimum battery capacity calculation unit: the service life requirement of the monitoring device is converted into a relational expression of the energy deficit and the minimum battery capacity, and a calculation formula of the minimum battery capacity is obtained through mathematical conversion, so that the minimum battery capacity is obtained.
Further, the acquisition interval setting module respectively calculates the theoretical maximum value T of the cycle time of one round of multi-parameter data acquisition according to the following formulamaxAnd a minimum value Tmin：
In the formula
The maximum collection interval of the data of the ith parameter]Representing the least common multiple, N representing the number of sensors, ciEnergy consumed for acquisition and processing of the ith parameter, piFor the probability of abnormality of the ith parameter data in the history statistics, τiFor a reduced proportion of the acquisition interval of the ith parameter data within a data acquisition cycle, CrealThe energy consumption of the monitoring device in one day, and delta t is the unit time slot length of a CPU of the monitoring device;
and according to the real-time performance of data monitoring and the service life requirement of the monitoring device, the following constraint relation is obtained by combining a number theory:
solving the above formula by a greedy algorithm to obtain the data acquisition interval of the ith parameter to be actually set
Furthermore, the device also comprises a priority setting module which is used for selecting and scheduling the sensor through priority when the data acquisition time slots conflict, and avoiding causing the thread blockage of the processor. When the data acquisition time slots conflict, the data acquisition can be carried out through the variable weight counter and the priority joint scheduling.
According to a third aspect of the invention, there is provided an electronic device comprising a processor; a memory for storing one or more programs which, when executed by the processor, cause the processor to carry out the method according to the first aspect of the invention.
According to a fourth aspect of the invention, there is provided a computer readable storage medium for storing a computer program which, when executed by a processor, performs the method according to the first aspect of the invention.
Has the advantages that:
1. the solar energy collected by the monitoring device from the outside has strong randomness in quantity, and in addition, an abnormal event in the monitoring process also has emergencies, so that the exact theoretical battery capacity meeting the expected service life of the monitoring device cannot be determined.
2. When the battery capacity actually configured by the monitoring device is larger than the minimum capacity requirement, the data acquisition interval strategy of the invention can avoid data acquisition conflict to the maximum extent and improve the real-time property of the monitoring data by setting prime number acquisition intervals for various monitoring parameters.
3. The setting of the processor time sequence allocation algorithm can deal with the burstiness of different types of abnormal data, the abnormal data can be collected and tracked in time, and meanwhile, the data collection priority can provide a sensor scheduling basis for the time slot conflict of different parameter data collection.
Drawings
FIG. 1 is a flow chart of a method of data acquisition optimization according to an embodiment of the present invention;
FIG. 2 is a flow chart of minimum capacitance calculation according to an embodiment of the present invention;
FIG. 3 is a flow chart of data acquisition interval setting according to an embodiment of the present invention;
FIG. 4 is a diagram of a conventional mode slot allocation strategy according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a time slot allocation strategy in burst mode according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a prioritization policy according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a data acquisition optimization device according to another embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to yet another embodiment of the invention.
Detailed Description
The solution of the invention will now be further described with reference to the accompanying drawings. It should be understood that the following embodiments are provided only for the purpose of thoroughly and completely disclosing the present invention and fully conveying the technical concept of the present invention to those skilled in the art, and the present invention may be embodied in many different forms and is not limited to the embodiments described herein. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention.
As shown in fig. 1, in an embodiment, a data acquisition optimization method 10 for a multi-parameter monitoring sensor is provided, which only uses historical operating data and deployment parameters as input, can reasonably configure resources for a multi-parameter monitoring device, covers battery capacity configuration before equipment leaves a factory and acquisition settings in an operating process, and meets various requirements such as device life, data monitoring real-time performance, reduction of different parameter data acquisition time slot conflicts, and timely tracking and feedback of abnormal data, and includes the following steps:
step S11, using the deployment parameters and historical statistics, obtains the minimum battery capacity required to meet the basic performance requirements of the device.
The algorithm flow for the minimum battery capacity configuration is shown in fig. 2. Because the multi-parameter monitoring sensor device only has one processor, only data of one parameter can be acquired at the same time. The time required by single data acquisition is defined as delta t, in an actual scene, the data acquisition time is often far shorter than the data acquisition interval, the data acquisition intervals of various parameters can be regarded as integral multiples of the delta t, namely the unit time slot length of the system is set to be delta t (unit: second, order of magnitude is 10-100 milliseconds), and the system refers to a CPU of a monitoring device. In consideration of real-time performance of data monitoring, each type of parameter has respective maximum data acquisition interval requirements according to potential characteristics, and a three-parameter monitoring device is taken as an example. Defining three types of maximum data acquisition intervals as
The maximum theoretical duration of a data acquisition cycle (involving multiple acquisitions of different types of parameters) may be set to
One cycle means that the scheduling behavior is regular from the CPU point of view, the sensor scheduled i and the TthmaxThe + i scheduled sensors are identical, with the least common multiple being taken as an example, in the first category of sensors, which are scheduled in one cycle
Note again that the sensor scheduling is not sequential, but rather three independent, each with its own period, such as the a sensor, whose period is T1, which is activated as soon as time comes, independent of the other sensors, so that a period is exactly the least common multiple of the three.
Aiming at the condition that different types of parameters can be abnormal, the probability that each type of data is abnormal is p according to historical statistical data1，p2，p3. An anomaly is a data monitoring value that exceeds a certain threshold range, such as an excessive temperature. The abnormal data is calculated by a calculation unit in the monitoring device or other sink nodes or even a remote server, and abnormal operation is performed only when the real-time data is abnormal, wherein the probability is a statistical result obtained according to the probability that the abnormal data reported by the monitoring device occupies all data. Within a data acquisition cycle, shortening the acquisition interval of abnormal type data until the abnormal event is removed, and setting the shortening ratio to be tau respectively1，τ2，τ3。
Considering that the energy consumption of each acquisition and preprocessing of different parameter data is different, respectively defined as c1，c2，c3Calculating the power consumption of the monitoring device during the day, as shown in formula (1)
Assuming that the energy quantity obtained by the monitoring device through collecting solar energy in one day is E, the value of E may vary from day to day due to weather, electromagnetic wave propagation loss, energy storage and the like, but in a fixed season, E may be considered to be independently and equally distributed, and the complementary cumulative function thereof may be obtained through historical data fitting, may be fitted by using a least-weighted two-multiplication method, defined as Fe(x) In that respect Calculating the energy deficit of the detection device in one day, as in formula (2)
D＝C-E (2)
Defining the actual life of the monitoring device as L, the expected life as L, and the life requirements of the monitoring device as follows: the probability that the actual lifetime is greater than the expected lifetime is greater than the probability ε, as shown in equation (3)
Pr{L＞l}＞ε (3)
The minimum battery capacity required for the detection device is set to
Since the event { L > L } is equivalent to an event
By mathematical derivation equation (4)
Further, according to the set expected probability epsilon, the minimum battery capacity required by the monitoring device is calculated, as shown in formula (5)
Wherein
Is FeThe inverse function of (c). The minimum battery capacity obtained according to the formula (5) guarantees the service life of the monitoring device and the acquisition of monitoring dataThe real-time performance can be realized, the equipment configuration cost can be reduced, and in addition, the formula (5) is low in calculation complexity and can be applied to engineering configuration in different regions and different time periods.
And step S12, calculating the theoretical minimum value of the cycle time of primary data acquisition, and solving and setting the actual acquisition interval of the data according to a number theory based on the real-time requirement of monitoring and the service life requirement of the monitoring device.
Because the capacity value of the battery is discrete in the actual production process, the actually selected battery has capacity
Presence greater than engineering calculated value
The possibility of (2). Due to the fact that the battery energy is rich, the data acquisition interval of each parameter can be further reduced on the basis of guaranteeing the service life requirement of the monitoring device, and the real-time performance of data monitoring is improved. As shown in fig. 3, the specific implementation steps are as follows:
calculating to obtain the maximum energy consumption allowed by the monitoring device in one day according to the formula (5), as shown in the formula (6)
Because the joint type (1) can not solve the actual data acquisition interval of each parameter, a reasonable data acquisition interval is searched by adopting the thought of problem decomposition, and the theoretical minimum value of the cycle time of one-time data acquisition is firstly calculated, as shown in formula (7)
Defining the desired cyclic interval as TrealHaving a value of Tmin≤Treal≤Tmax. Next, define
For the actual parameter data acquisition interval to be set, the value of the parameter data acquisition interval needs to be considered: 1) in order to improve the real-time performance of data monitoring,
the smaller the value, the better; 2) in order to reduce the time slot conflict in the data acquisition process, according to the theory of number theory correlation,
the value is required to be prime as much as possible; 3) in order to meet the requirements of the service life of the monitoring device,
there is a minimum value. In view of the above, it is desirable to provide,
the calculation of (a) should satisfy the following requirements, as in formula (8):
the greedy algorithm may be used to solve equation 8, taking into account
The value is not very large, so the number of the primes meeting the requirement is very small, and the pair
The largest prime number as possible is taken to start the attempt, and the algorithm can converge quickly due to the constraint of the condition 2 of equation (8). In addition, if
There is a failure to find a proper prime number combinationThen the configuration result with the largest number of preserved prime numbers and the largest value of prime numbers is obtained.
And finally, calculating the cycle time of one-time actual data acquisition:
and step S13, allocating the acquisition time slot of the monitoring device in a time division multiplexing mode.
Currently, a mainstream multi-parameter monitoring sensor device only has one processor, and only one type of parameter data acquisition task can be executed at the same time, so that the sensor is required to be scheduled in a time division multiplexing mode. The time slot allocation is divided into a conventional mode and a burst mode, the burst mode is set to deal with the abnormal data condition, and the abnormal condition is guaranteed to be tracked and reported in time by reducing the data acquisition interval of abnormal parameters. The specific flow is as follows:
referring to FIG. 4, in the conventional mode, first, a system time slice is divided, the system time is defined as t, and a remainder operator is defined as mod when
When the system is used, the sensor 1 is controlled to collect data; when in use
Meanwhile, controlling the sensor 2 to collect data; when in use
The control sensor 3 collects data.
When in use
And when at least two operation results are 0, controlling the collected data with the highest priority in the corresponding sensor.
The processor preprocesses the acquired data, continues to execute a conventional time slot allocation mode when the data are normal, and executes a burst time slot allocation mode when abnormal data are found.
Referring to fig. 5, in the burst mode, the priority of the sensor corresponding to the abnormal parameter is set to be the maximum, the data acquisition interval is shortened, taking the abnormal data acquired by the sensor 1 as an example, the data acquisition interval is
(0＜τ1< 1), the scheduling priority is adjusted to be highest, and the acquisition mode is similar to the conventional mode. The sensor scheduling interval for acquiring the abnormal data is reduced, so that the large-scale and high-frequency acquisition of the related parameter data is facilitated, the state change of the monitored object is accurately tracked and analyzed, and the state change is reported to a management system in time.
In burst mode, when tmodTrealAnd when the data is 0, judging whether the data has the abnormality or not, if the data has the abnormality, returning to the normal mode, and if the data still has the abnormality, continuously executing the burst time slot allocation mode in the next data acquisition cycle. Since the monitoring device consumes more energy in the burst mode than in the normal mode, returning to the normal mode after the abnormal data does not exist contributes to energy saving, extending the life of the monitoring device.
And step S14, when the data acquisition time slots of the monitoring devices conflict, scheduling data acquisition by setting a variable weight calculator and a priority level.
The scheduling priority is a necessary means for processing scheduling conflict, and a scheduling sensor is selected according to the priority when the data acquisition time slots conflict, so that the thread of the processor is prevented from being blocked; respectively defining the scheduling priority of each sensor as q1，q2，q3If there is no same value, the value set is {1,2,3,4,5,6}, and the higher the digital value is, the higher the corresponding priority is, where {1,2,3} is the priority of the normal mode, and {4,5,6} is the priority to be forcibly given to the sensor corresponding to the abnormal data in the burst mode, and referring to fig. 6, the scheduling priority in the burst mode is set as follows:
firstly, calculating the abnormal probability p of various parameter data according to the collected historical data1，p2，p3Go forward and go forwardOne step of initializing q according to the magnitude of these probabilities1，q2，q3. For example, if p2＞p1＞p3Then { q1,q2,q3}＝{2,3,1}。
In the data acquisition process under the conventional mode, in order to take fairness into consideration, a variable weight calculator and priority level are arranged for combining schedule data acquisition, and a weight counter, namely {1,2,3} → { w } is attached to each priority level1,w2,w3The initial value of the weight counter is 0, when acquisition conflict occurs, taking collision of two sensors as an example, the priority of the sensors is firstly compared with the weight i + wi,j+wj1) if i > j and i + wi≥j+wjThe weight value is then updated with a constant weight change Δ ω, specifically w for a sensor with a priority of 11Is updated to w1＝w1+ Δ ω; for a sensor with priority 2, the weight is not changed, w2≡ 0, for a sensor with priority 3, the weight is updated to w3＝w3- Δ ω. 2) If i > j but i + wi＜j+wjThen the two sensors exchange priorities, and wiAnd wjReset to 0. 3) If all three sensors conflict, the priority update can be performed in the same way.
In the burst mode, the priority of the sensor i in which abnormal data occurs first is set to 4, and the weight counter is not updated in the burst mode. If other sensors j also monitor abnormal data before the data abnormality of the sensor i is not recovered, the priority of j is set to be 5, and the like. And after all the monitoring data are recovered to be normal, the processor enters a normal mode, the priority of the sensor is kept consistent with that of the sensor in the previous monitoring cycle, and the weight counters are all reset to be 0.
Referring to fig. 7, according to another embodiment of the present invention, there is provided a data acquisition optimization device 20 of a multi-parameter monitoring device, the device comprising:
the minimum battery capacity calculation module 21 is used for obtaining the minimum battery capacity required by meeting the basic performance requirement of the monitoring device according to the deployment parameters and the historical statistical data;
the acquisition interval setting module 22 is used for calculating the theoretical minimum value of the cycle time of primary data acquisition, and solving the actual acquisition interval of the data according to a number theory based on the real-time requirement of monitoring and the service life requirement of the monitoring device;
the time slot allocation module 23 is used for setting an acquisition strategy of the monitoring device through a modulus operation according to the set actual data acquisition interval and the set current acquisition time, and allocating a sensor to acquire data when the modulus operation result of the sensor is zero and no time slot conflict exists;
and the priority setting module 24 is used for coordinating and scheduling data acquisition by setting the variable weight calculator and the priority when the data acquisition time slots of the sensors conflict.
In a specific implementation, the minimum battery capacity calculation module 21 may include:
the electric energy consumption estimation unit 2101 is used for calculating the abnormal probability of various parameter data according to historical data and estimating the electric energy consumption of the monitoring device in one day based on the abnormal probability and the maximum acquisition interval requirement of various data;
taking a three-parameter monitoring device as an example, the calculation formula is as follows:
An energy deficit calculation unit 2102 for obtaining an energy deficit of the monitoring device according to the solar energy collected by the monitoring device within one day and the estimated electric energy consumption;
by fitting a function Fe(x) And fitting the energy quantity E obtained by collecting solar energy of the monitoring device in one day, and calculating the energy deficit of the monitoring device in one day, wherein D is C-E.
Minimum battery capacity calculation unit 2103: the service life requirement of the monitoring device is converted into a relational expression of the energy deficit and the minimum battery capacity, and a calculation formula of the minimum battery capacity is obtained through mathematical conversion, so that the minimum battery capacity is obtained.
Setting the actual life of the monitoring device as L and the expected life as L, and setting the life requirement of the monitoring device as follows: the probability that the actual life is longer than the expected life is larger than the probability epsilon, namely Pr { L > L } > epsilon, and the minimum battery capacity required by the detection device is set as
Since the event { L > L } is equivalent to an event
Derived by mathematical derivation
According to the set expected probability epsilon, calculating the minimum battery capacity required by the monitoring device,
wherein
Is FeThe inverse function of (c).
The acquisition interval setting module 22 calculates the theoretical minimum value of the one-time data acquisition cycle length according to the following formula:
defining the requested acquisition cycle interval as TrealHaving a value of Tmin≤Treal≤Tmax. Next, define
For the actual parameter data acquisition interval to be set, the value of the parameter data acquisition interval needs to be considered: 1) in order to improve the real-time performance of data monitoring,
the smaller the value, the better; 2) in order to reduce the time slot conflict in the data acquisition process, according to the theory of number theory correlation,
the value is required to be prime as much as possible; 3) in order to meet the requirements of the service life of the monitoring device,
there is a minimum value. In view of the above, it is desirable to provide,
the calculation of (a) should satisfy the following requirements, as follows:
solving the above formula by a greedy algorithm, wherein the prime number is far less than the non-prime number, so that the solution is carried out
The calculation complexity of (2) is not high, and the algorithm can be converged quickly. If it is
If the proper prime number combination can not be found, the reserved prime number is fetchedThe configuration result with the largest number and the largest prime number value. Finally, obtaining the cycle time of one-time actual data acquisition:
the time slot allocation module 23 has two modes of operation: the data monitoring method comprises a normal mode and a burst mode, wherein the normal mode is applied to a normal acquisition situation, and the burst mode deals with a data abnormal situation, as mentioned above, a data monitoring value of an abnormal finger exceeds a certain threshold range, such as overhigh temperature. The processor preprocesses the acquired data, continues to execute a conventional time slot allocation mode when the data are normal, and executes a burst time slot allocation mode when abnormal data are found.
Specifically, in the normal mode, the current time t of the system is obtained, and the actual data acquisition interval of each parameter is known at the moment
Define the remainder operator as mod when
When the system is used, the sensor 1 is controlled to collect data; when in use
Meanwhile, controlling the sensor 2 to collect data; when in use
The control sensor 3 collects data. When in use
And when at least two operation results are 0, controlling the collected data with the highest priority in the corresponding sensor.
In the burst mode, the priority of the sensor corresponding to the abnormal parameter is set to be maximum, the data acquisition interval is shortened, taking the abnormal data acquired by the sensor 1 as an example, the data acquisition interval is
(0＜τ1< 1), the scheduling priority is adjusted to be highest, and the acquisition mode is similar to the conventional mode. The sensor scheduling interval for acquiring the abnormal data is reduced, so that the large-scale and high-frequency acquisition of the related parameter data is facilitated, the state change of the monitored object is accurately tracked and analyzed, and the state change is reported to a management system in time.
In burst mode, when tmodTrealAnd when the data is 0, judging whether the data has the abnormality or not, if the data has the abnormality, returning to the normal mode, and if the data still has the abnormality, continuously executing the burst time slot allocation mode in the next data acquisition cycle. Since the monitoring device consumes more energy in the burst mode than in the normal mode, returning to the normal mode after the abnormal data does not exist contributes to energy saving, extending the life of the monitoring device.
The priority setting module 24 is configured to select a scheduling sensor according to priority when data acquisition time slots conflict, so as to avoid causing processor thread blocking. Setting scheduling priority q for each monitoring sensor1，q2，q3The value of the parameter data is unique, and the initial value can be initialized according to the probability of abnormality of various parameter data. For example, if p2＞p1＞p3Then { q1,q2,q 32,3,1, in this example, a higher numerical value indicates a higher corresponding priority. Similarly, different value ranges can be set for the normal mode and the burst mode, and the priority range in the burst mode is higher than the priority in the normal mode, for example, the set of the priority values of the three monitoring devices can be set to {1,2,3,4,5,6}, where {1,2,3} is the priority in the normal mode, and {4,5,6} is the priority to be forcibly given to the sensor corresponding to the abnormal data in the burst mode.
Further, for fairness, a variable weight calculator and a priority level can be set to combine schedule data acquisition, and a weight counter, namely {1,2,3} → { w } is attached to each priority level1,w2,w3The initial value of the weight counter is 0, when acquisition conflict occurs, taking collision of two sensors as an example, the priority and the weight of the sensors are compared firstlyHeavy i + wi,j+wj1) if i > j and i + wi≥j+wjThe weight value is then updated with a constant weight change Δ ω, specifically w for a sensor with a priority of 11Is updated to w1＝w1+ Δ ω; for a sensor with priority 2, the weight is not changed, w2≡ 0, for a sensor with priority 3, the weight is updated to w3＝w3- Δ ω. 2) If i > j but i + wi＜j+wjThen the two sensors exchange priorities, and wiAnd wjReset to 0. 3) If all three sensors conflict, the priority update can be performed in the same way.
In the burst mode, the priority of the sensor i in which abnormal data occurs first is set to 4, and the weight counter is not updated in the burst mode. If other sensors j also monitor abnormal data before the data abnormality of the sensor i is not recovered, the priority of j is set to be 5, and the like. And after all the monitoring data are recovered to be normal, the processor enters a normal mode, the priority of the sensor is kept consistent with that of the sensor in the previous monitoring cycle, and the weight counters are all reset to be 0.
FIG. 8 shows a schematic block diagram of an example device 30 that may be used to implement an embodiment of the invention. The device 30 includes a Central Processing Unit (CPU)31 that can perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)32 or computer program instructions loaded from a storage unit 38 into a Random Access Memory (RAM) 33. In the RAM 33, various programs and data necessary for the operation of the device 30 can also be stored. The CPU 31, ROM 32, and RAM 33 are connected to each other via a bus 34. An input/output (I/O) interface 35 is also connected to bus 34.
A number of components in the device 30 are connected to the I/O interface 35, including: an input unit 36 such as a keyboard, a mouse, etc.; an output unit 37 such as various types of displays, speakers, and the like; a storage unit 38 such as a magnetic disk, an optical disk, or the like; and a communication unit 39 such as a network card, modem, wireless communication transceiver, etc. The communication unit 39 allows the device 30 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit 31 performs the respective methods and processes described above. For example, in some embodiments, process 10 may be implemented as a computer software program tangibly embodied in a computer-readable medium, such as storage unit 38. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 30 via ROM 32 and/or communications unit 39. One or more steps of the method 10 described above may be performed when the computer program is loaded into the RAM 33 and executed by the CPU 31.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing associated hardware, and the program may be stored in a computer-readable storage medium. In the context of the present invention, the computer-readable medium may be considered tangible and non-transitory. Non-limiting examples of a non-transitory tangible computer-readable medium include a non-volatile memory circuit (e.g., a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), a volatile memory circuit (e.g., a static random access memory circuit or a dynamic random access memory circuit), a magnetic storage medium (e.g., an analog or digital tape or hard drive), and an optical storage medium (e.g., a CD, DVD, or blu-ray disc), among others.
Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Further, while operations are depicted in a particular order, this should not be understood as necessarily requiring their performance in the particular order shown. In certain circumstances, multitasking and parallel processing may be advantageous, or one or more of the tasks may be omitted, or performed in other sequences. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
The foregoing is directed to exemplary embodiments of the present invention, and it is to be understood that the invention is not limited to the specific embodiments disclosed. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A data acquisition optimization method of a multi-parameter monitoring device is characterized by comprising the following steps:
obtaining the minimum battery capacity required by meeting the basic performance requirement of the device by using the deployment parameters and the historical statistical data, and configuring the actual battery capacity of the monitoring device based on the minimum battery capacity;
calculating theoretical maximum and minimum values of the cycle time of primary data acquisition, and solving and setting actual acquisition intervals of data according to a number theory based on real-time requirements of monitoring and service life requirements of a monitoring device; and
the acquisition time slots of the monitoring devices are allocated in a time division multiplexing mode,
the actual acquisition interval setting method of the data comprises the following steps:
respectively calculating theoretical maximum value T of cycle duration of one-round multi-parameter data acquisitionmaxAnd a minimum value Tmin：
In the formula
The maximum collection interval of the data of the ith parameter]Representing the least common multiple, N representing the number of sensors, ciEnergy consumed for acquisition and processing of the ith parameter, piFor the probability of abnormality of the ith parameter data in the history statistics, τiFor a reduced proportion of the acquisition interval of the ith parameter data within a data acquisition cycle, CrealThe energy consumption of the monitoring device in one day, and delta t is the unit time slot length of a CPU of the monitoring device;
order to
The following constraint relationship is obtained for the data acquisition interval of the ith parameter to be set actually according to the real-time performance of data monitoring and the service life requirement of the monitoring device by combining a number theory:
solving the above formula by greedy algorithm to obtain
2. The method of claim 1, wherein the step of using the deployment parameters and historical statistics to obtain the minimum battery capacity required to meet the basic performance requirements of the device comprises the steps of:
calculating the abnormal probability of various parameter data according to historical data, and estimating the electric energy consumption of the monitoring device in one day based on the abnormal probability and the maximum acquisition interval requirement of various data;
obtaining the energy deficit of the monitoring device according to the solar energy collected by the monitoring device within one day and the estimated electric energy consumption;
the service life requirement of the monitoring device is converted into a relational expression of the energy deficit and the minimum battery capacity, a calculation formula of the minimum battery capacity is obtained through mathematical conversion, and the minimum battery capacity is obtained through calculation.
3. The method of claim 1, wherein the allocating acquisition time slots of the monitoring devices in a time division multiplexed manner comprises: and performing modulus operation according to the actual data acquisition interval and the current acquisition time, and distributing the data acquired by the sensor when the modulus operation result of one sensor is zero and no time slot conflict exists.
4. The method of claim 1, further comprising: preprocessing the acquired data, and increasing the priority of the sensor corresponding to the abnormal parameters and shortening the data acquisition interval when the data are found to be abnormal.
5. The method of claim 3, further comprising: when the data acquisition time slots of the sensors conflict, the variable weight calculator and the priority level are set to cooperate to schedule data acquisition, and the specific method is as follows:
a weight counter is attached to each priority, and the priorities (i, j) and the weights (w) of the sensors are comparedi,wj) If i > j and i + wi≥j+wjIf so, increasing the weight variation delta omega of the sensor weight with low priority, and decreasing the weight variation delta omega of the sensor weight with high priority; if i > j and i + wi＜j+wjThen the priorities of the two sensors are exchanged and the weight w is weightediAnd wjReset to 0.
6. A data acquisition optimization device for a multi-parameter monitoring device, the device comprising:
the minimum battery capacity calculation module is used for obtaining the minimum battery capacity required by meeting the basic performance requirement of the monitoring device according to the deployment parameters and the historical statistical data;
the acquisition interval setting module is used for calculating the theoretical maximum value and the theoretical minimum value of the cycle time of primary data acquisition, solving and setting the actual acquisition interval of the data according to a number theory based on the real-time requirement of monitoring and the service life requirement of the monitoring device;
a time slot distribution module which distributes the collection time slot of the monitoring device in a time division multiplexing mode,
the acquisition interval setting module respectively calculates the theoretical maximum value T of the cycle time of one round of multi-parameter data acquisition according to the following formulamaxAnd a minimum value Tmin：
In the formula
The maximum collection interval of the data of the ith parameter]Representing the least common multiple, N representing the number of sensors, ciEnergy consumed for acquisition and processing of the ith parameter, piFor the probability of abnormality of the ith parameter data in the history statistics, τiFor a reduced proportion of the acquisition interval of the ith parameter data within a data acquisition cycle, CrealThe energy consumption of the monitoring device in one day, and delta t is the unit time slot length of a CPU of the monitoring device;
and according to the real-time performance of data monitoring and the service life requirement of the monitoring device, the following constraint relation is obtained by combining a number theory:
solving the above formula by a greedy algorithm to obtain the data acquisition interval of the ith parameter to be actually set
7. The apparatus of claim 6, wherein the minimum battery capacity calculation module comprises:
the electric energy consumption estimation unit is used for calculating the abnormal probability of various parameter data according to historical data and estimating the electric energy consumption of the monitoring device in one day based on the abnormal probability and the maximum acquisition interval requirement of various data;
the energy deficit calculation unit is used for obtaining the energy deficit of the monitoring device according to the solar energy collected by the monitoring device within one day and the estimated electric energy consumption;
minimum battery capacity calculation unit: the service life requirement of the monitoring device is converted into a relational expression of the energy deficit and the minimum battery capacity, and a calculation formula of the minimum battery capacity is obtained through mathematical conversion, so that the minimum battery capacity is obtained.
8. The apparatus of claim 6, further comprising a priority setting module for avoiding processor thread blocking by selecting a scheduling sensor according to priority when data collection time slots conflict.
9. An electronic device, characterized in that the device comprises:
a processor; a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one processor, the programs when executed by the processor implementing the method of any of claims 1-5.
10. A computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of any one of claims 1-5.
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