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CN-122002301-A - Energy acquisition-oriented wireless sensor network coverage scheduling method and system

CN122002301ACN 122002301 ACN122002301 ACN 122002301ACN-122002301-A

Abstract

The invention belongs to the technical field of wireless sensor network application, and discloses an energy-acquisition-oriented wireless sensor network coverage scheduling method and system, wherein the method comprises the steps of constructing a daily multi-scene power track according to historical weather and photovoltaic data acquired by each sensor, introducing tail risk measurement to evaluate the track, outputting a daily predicted power curve with controllable risk, and calculating the daily energy budget of each sensor; dividing a daily time slot into different energy balance periods according to a daily predicted power curve, establishing available energy constraint in the balance periods, gridding a preset monitoring area, evaluating the cooperative detectable potential of grids by combining the energy budget of each sensor, selecting grids meeting the continuity constraint in a preset space column structure to form a continuous target grid chain, and remarkably improving the coverage quality, the energy utilization rate and the sustainability of a wireless sensor network.

Inventors

  • XU PEI

Assignees

  • 合肥大学

Dates

Publication Date
20260508
Application Date
20260409

Claims (10)

  1. 1. The energy acquisition-oriented wireless sensor network coverage scheduling method is characterized by comprising the following steps of: S1, constructing a daily multi-scene power track according to historical weather and photovoltaic data acquired by each sensor, introducing tail risk measurement to evaluate the track, outputting a daily predicted power curve with controllable risk, and calculating the current daily energy budget of each sensor; S2, dividing a daily time slot into different energy balance periods according to a daily predicted power curve, and establishing available energy constraint in the balance periods; S3, gridding a preset monitoring area, and evaluating the cooperative detectable potential of the grids by combining the energy budget of each sensor; s4, calculating the cooperative coverage quality of each grid in the target grid chain in each time slot, searching a grid-time combination with the minimum cooperative coverage quality in the combination range of the target grid chain and the time slot, and identifying the time-space bottleneck point with the weakest coverage; S5, under the constraint of available energy, constructing a working schedule for each sensor by taking the cooperative coverage quality of space-time bottleneck points as a leading target, repairing the schedule with energy overrun, carrying out fusion scoring on the schedule under different energy budgets to obtain an optimal schedule, and executing monitoring tasks and reporting an operation state by each sensor according to the optimal working schedule.
  2. 2. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 1, wherein the method for constructing a daily multi-scenario power track comprises: the method comprises the steps that continuous collection is carried out through a meteorological sensor and a photovoltaic output monitoring unit which are arranged in a monitoring area, wherein the meteorological sensor is used for acquiring meteorological data, the photovoltaic output monitoring unit is used for acquiring photovoltaic output power data at corresponding moments, and uniform time identification is given to the meteorological data and the photovoltaic output power data; Performing time stamp alignment, outlier rejection, missing value interpolation and sampling interval unified processing on the meteorological data and the photovoltaic output power data to form a one-to-one corresponding sample sequence under the same time scale; taking the normalized weather state indexes and the change trend of the weather state indexes as historical samples, and counting the average value, variance and quantile of the historical samples on index values and change rates, wherein the index values refer to the weather state indexes at a single moment, and the change rates refer to the change rates of the weather state indexes among continuous time slots; by representing each historical sample as a sample vector, the sample vector comprises a meteorological state index value of the sample in each time slot and a corresponding meteorological state index change rate; If the cosine similarity of the two sample vectors is larger than or equal to a preset cosine similarity threshold, judging that the two samples have similar meteorological action mechanism and photovoltaic power response characteristics; dividing all historical samples into different meteorological scene types according to a cosine similarity calculation result, wherein each meteorological scene type corresponds to a group of similar samples, and giving scene identification and occurrence frequency weight; aiming at each meteorological field scene type, a theoretical output envelope curve under the condition of no meteorological disturbance is constructed by combining a theoretical irradiation intensity rule corresponding to solar altitude change in the day, and a power track in the day is generated according to the power change characteristics of a historical sample; And combining the scene tracks according to time sequence, and branching according to the occurrence frequency weight to form a daily multi-scene photovoltaic power scene tree, wherein each branch represents a possible power evolution path, so that the daily multi-scene photovoltaic power track is formed.
  3. 3. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 2, wherein the method for calculating the current day energy budget of each sensor comprises: The method comprises the steps of (1) evaluating each candidate power track in a daily multi-scene photovoltaic power track by introducing tail risk measurement, forming a comprehensive evaluation function by combining energy utilization effect and light discarding loss, and performing risk perception scoring on each candidate power track through the comprehensive evaluation function; And according to the daily predicted power curve, acquiring power values corresponding to the sensors in each time slot, combining rated capacity of each sensor, and calculating and acquiring the current day energy budget of each sensor.
  4. 4. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 3, wherein the method of establishing available energy constraints within a balancing period comprises: Dividing the intra-day predicted power curve into different continuous time slots according to a time sequence, wherein each time slot corresponds to a fixed time interval; dividing adjacent time slots into different energy balance periods according to the accumulation condition of available energy of the sensor according to a daily predicted power curve, so that the total available energy in each balance period meets the sum of predicted power requirements in the energy balance period; and in each energy balance period, establishing available energy constraint for each sensor, wherein the available energy constraint comprises that the total energy consumption of the sensor in the energy balance period is smaller than or equal to the available energy budget corresponding to the sensor in the energy balance period, and the power consumption of the sensor in each time slot is smaller than or equal to the predicted power of the sensor corresponding to the time slot, so that a scheduling time organization frame is formed.
  5. 5. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 4, wherein the method of budget assessment grid for collaborative detectable potential comprises: Based on the spatial position relation between the sensors and the grid cells and a preset probability perception model, calculating single-node detection contribution of each sensor to the grid cells under preset different sensing radius grades; For each grid unit, selecting a feasible radius grade meeting the energy consumption of the current day energy budget which is not less than the sensing radius grade from the selectable sensing radius grade set for each sensor; selecting a radius grade with the largest single-node detection contribution to the grid unit from the feasible radius grades as the effective detection contribution of the sensor to the grid unit; and accumulating the effective detection contributions of all the sensors to obtain the cooperative detectable potential of the grid unit under the energy constraint condition.
  6. 6. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 5, wherein the method of forming a continuous target mesh chain comprises: For each column of grid cells, screening the grid cells meeting the energy feasibility as candidate grid cells according to the cooperative detectable potential of each grid cell under the energy constraint condition and the minimum energy required by each sensor to maintain work on each grid cell; And sequentially selecting one target grid from the candidate grid units in each column, wherein in the selection process, the space distance between the target grids selected by the adjacent columns is smaller than or equal to a preset space distance threshold value, and simultaneously, the grids with the cooperative detectable potential larger than the preset cooperative detectable potential threshold value are preferentially selected, so that a continuous grid sequence from the first column to the last column is obtained, and a continuous target grid chain is formed.
  7. 7. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 6, wherein the method of identifying the weakest coverage spatio-temporal bottleneck point comprises: For each grid in the formed continuous target grid chain, fusing single-node detection contributions of all sensors according to a preset cooperative fusion rule in each time slot in a prediction day to obtain cooperative coverage quality of the grid in the time slot; searching a grid and a time point with the smallest cooperative coverage quality in the combination range of the continuous target grid chain and the time slot as a grid-time combination, and covering the grid-time combination with the weakest space-time bottleneck point.
  8. 8. The energy harvesting-oriented wireless sensor network coverage scheduling method of claim 7, wherein the method of obtaining an optimal schedule comprises: Respectively establishing a scheduling search space for each sensor, coding the working state of each time slot of the sensor in a prediction day and the corresponding perceived radius grade combination as a candidate scheduling individual, and respectively maintaining a local row Cheng Chongqun for each sensor; Performing evolution search on the individual candidate scheduling by performing iterative execution of selection, intersection, mutation and elite retention operation, taking the improvement degree of collaborative coverage quality at space-time bottleneck points as an optimization target in the evolution process, and optimizing the collaborative coverage quality of a continuous target grid chain on the premise of ensuring that the optimization target is not reduced, thereby forming a word order double-target optimization mechanism and obtaining the local optimal scheduling of a single sensor; The method comprises the steps that candidate scheduling individuals with energy consumption exceeding available energy budget corresponding to an energy balance period in the evolution process are subjected to gradual reduction or reduction of working grades until the available energy budget is met according to unit energy coverage gains corresponding to each time slot and each perception radius grade, and scheduling feasibility restoration is achieved; And exchanging elite schedule abstract information in a low-frequency mode in the evolution process of each sensor, carrying out fusion scoring on the local optimal schedules obtained under different energy budget conditions after the evolution reaches a preset termination condition to obtain comprehensive scores of the scheduled individuals, and finally selecting the local optimal schedule with the highest comprehensive score of the scheduled individuals as the optimal schedule of each sensor.
  9. 9. The energy-harvesting-oriented wireless sensor network coverage scheduling method of claim 8, wherein the method for each sensor to execute a monitoring task and report an operation state according to an optimal operation schedule comprises: The method comprises the steps of obtaining optimal schedule, executing monitoring tasks according to the schedule-determined perception radius grade in each time slot in the day by each sensor, presetting a working time slot and a dormant time slot, in the working time slot, monitoring data acquisition and target detection are carried out on grid units covered by a continuous target grid chain by the sensors, entering a standby state by the sensors in the dormant time slot, and reporting the acquired monitoring data and the running state of the sensors to a network control terminal through a preset communication strategy.
  10. 10. An energy-harvesting-oriented wireless sensor network coverage scheduling system for implementing the energy-harvesting-oriented wireless sensor network coverage scheduling method of any one of claims 1 to 9, comprising: the risk perception prediction module is used for constructing a daily multi-scene power track according to historical weather and photovoltaic data acquired by each sensor, introducing tail risk measurement to evaluate the track, outputting a daily predicted power curve with controllable risk, and calculating the current daily energy budget of each sensor; The energy balance construction module is used for dividing a daily time slot into different energy balance periods according to a daily predicted power curve and establishing available energy constraint in the balance periods; The target chain selection module is used for gridding a preset monitoring area, and evaluating the cooperative detectable potential of grids by combining the energy budget of each sensor; The space-time bottleneck recognition module is used for calculating the cooperative coverage quality of each grid in the target grid chain in each time slot, searching a grid-time combination with the minimum cooperative coverage quality in the combination range of the target grid chain and the time slot, and recognizing the space-time bottleneck point with the weakest coverage; The distributed scheduling module is used for constructing working schedules for the sensors by taking the cooperative coverage quality of space-time bottleneck points as a leading target under the available energy constraint, repairing the schedules with energy overrun, carrying out fusion scoring on the schedules under different energy budgets to obtain an optimal schedule, and executing monitoring tasks and reporting running states by the sensors according to the optimal working schedules.

Description

Energy acquisition-oriented wireless sensor network coverage scheduling method and system Technical Field The invention relates to the technical field of wireless sensor network application, in particular to an energy acquisition-oriented wireless sensor network coverage scheduling method and system. Background The existing wireless sensor network coverage scheduling method and system mainly have the following problems: Wireless sensor networks have received a great deal of research and application attention as a result of their wide application in a variety of fields. In actual deployment, the sensor nodes are powered by a limited battery, and the network operation is energy constrained. In recent years, energy collection type wireless sensor networks become research hotspots, and renewable energy collection such as photovoltaic can alleviate node energy limitation. However, the existing wireless sensor network coverage scheduling method and system have a plurality of defects. The existing method only provides a single power prediction curve, photovoltaic power deviation under extreme weather is not considered, tail risks are difficult to quantify, energy gaps or light abandoning occur in actual application, and reliability of network coverage scheduling is reduced. And secondly, the energy budget of the traditional node depends on historical average power or experience value, dynamic change and fluctuation of photovoltaic power are ignored, so that energy distribution is unreasonable, and continuous operation of the node is difficult to ensure. In addition, the prior art lacks a quantification method for the waste light energy, and the waste light cannot be effectively controlled. The traditional method does not consider the energy consumption difference of different working modes of the nodes, so that the coverage problem of the network occurs. The existing method depends on experience or a static model, and lacks consideration of node synergism, so that coverage optimization lacks scientific basis. Meanwhile, grid cells are not dynamically selected by combining with feasible perception radius, coverage scheduling cannot be adaptively adjusted, and long-term operation reliability of the network is reduced. In view of this, the present invention proposes an energy harvesting-oriented wireless sensor network coverage scheduling method to solve the above-mentioned problems. Disclosure of Invention In order to overcome the defects in the prior art and achieve the purposes, the invention provides the following technical scheme that the wireless sensor network coverage scheduling method for energy collection comprises the following steps: S1, constructing a daily multi-scene power track according to historical weather and photovoltaic data acquired by each sensor, introducing tail risk measurement to evaluate the track, outputting a daily predicted power curve with controllable risk, and calculating the current daily energy budget of each sensor; S2, dividing a daily time slot into different energy balance periods according to a daily predicted power curve, and establishing available energy constraint in the balance periods; S3, gridding a preset monitoring area, and evaluating the cooperative detectable potential of the grids by combining the energy budget of each sensor; s4, calculating the cooperative coverage quality of each grid in the target grid chain in each time slot, searching a grid-time combination with the minimum cooperative coverage quality in the combination range of the target grid chain and the time slot, and identifying the time-space bottleneck point with the weakest coverage; S5, under the constraint of available energy, constructing a working schedule for each sensor by taking the cooperative coverage quality of space-time bottleneck points as a leading target, repairing the schedule with energy overrun, carrying out fusion scoring on the schedule under different energy budgets to obtain an optimal schedule, and executing monitoring tasks and reporting an operation state by each sensor according to the optimal working schedule. Preferably, the method for constructing the intra-day multi-scene power track comprises the following steps: the method comprises the steps that continuous collection is carried out through a meteorological sensor and a photovoltaic output monitoring unit which are arranged in a monitoring area, wherein the meteorological sensor is used for acquiring meteorological data, the photovoltaic output monitoring unit is used for acquiring photovoltaic output power data at corresponding moments, and uniform time identification is given to the meteorological data and the photovoltaic output power data; Performing time stamp alignment, outlier rejection, missing value interpolation and sampling interval unified processing on the meteorological data and the photovoltaic output power data to form a one-to-one corresponding sample sequence under the same time scale; taking the