Meteorological monitoring network monitoring node scheduling method and system based on environmental perception

Abstract

The embodiment of the invention provides a meteorological monitoring network monitoring node scheduling method and system based on environmental perception, and relates to the technical field of meteorological monitoring, and the method comprises the steps: firstly obtaining an environmental perception data set of a meteorological monitoring network coverage area, which covers the environmental state of the position of a monitoring node and the operation state information of the monitoring node; and generating comprehensive state evaluation information containing environmental suitability and monitoring node reliability characteristics based on the environmental perception data set. And determining a monitoring node scheduling priority sequence according to the information, and executing activation, dormancy or mobile scheduling operation according to the monitoring node scheduling priority sequence. And finally, according to feedback information of the monitoring nodes after scheduling, dynamically adjusting an environment perception data set acquisition range and a comprehensive state evaluation information generation rule, thereby realizing intelligent and flexible scheduling of the monitoring nodes, and improving resource utilization efficiency and monitoring data quality of a meteorological monitoring network.

Description

Technical Field

[0001] The present application relates to the field of meteorological monitoring technology, and in particular to a method and system for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception. Background Art

[0002] In the field of meteorological monitoring, a meteorological monitoring network consists of numerous monitoring nodes distributed across different regions, used to obtain real-time meteorological data. However, traditional meteorological monitoring network node scheduling methods are relatively fixed and simple, often only executing operations such as activation and dormancy of monitoring nodes based on preset time intervals or fixed regional divisions.

[0003] The above-mentioned traditional scheduling methods do not fully consider the actual environmental changes in the monitoring area and the operating conditions of the monitoring nodes themselves. For example, in areas with complex terrain or changeable weather conditions, fixed scheduling methods may cause some monitoring nodes to be unable to work effectively when environmental conditions are poor, or in relatively stable areas, monitoring nodes are continuously in a high-load operating state, resulting in a waste of resources. At the same time, as the scope of the monitoring area expands and monitoring needs increase, traditional scheduling methods are difficult to adapt flexibly and cannot efficiently allocate monitoring resources, affecting the accuracy and timeliness of meteorological monitoring data. Therefore, a more intelligent and flexible monitoring node scheduling method is needed. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method and system for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception.

[0005] In conjunction with the first aspect of the present application, a method for scheduling monitoring nodes in a meteorological monitoring network based on environment perception is provided, which is applied to a meteorological monitoring network monitoring node scheduling system based on environment perception. The method includes:

[0006] Acquire an environmental perception data set for an area covered by a meteorological monitoring network, wherein the environmental perception data set includes environmental status information of a location where a monitoring node is located and operating status information of the monitoring node itself;

[0007] Generate comprehensive status assessment information of the monitoring node based on the environmental perception data set, wherein the comprehensive status assessment information includes environmental adaptability characteristics and monitoring node reliability characteristics;

[0008] Determining a scheduling priority sequence of monitoring nodes based on the comprehensive status evaluation information;

[0009] Executing activation, sleep or mobile scheduling operations of monitoring nodes according to the scheduling priority sequence;

[0010] The acquisition scope of the environmental perception data set and the generation rule of the comprehensive status evaluation information are adjusted according to the feedback information of the monitoring nodes after the scheduling operation.

[0011] In combination with the second aspect of the present application, a meteorological monitoring network monitoring node scheduling system based on environmental perception is provided, wherein the meteorological monitoring network monitoring node scheduling system based on environmental perception includes a machine-readable storage medium and a processor, wherein the machine-readable storage medium stores machine-executable instructions. When the processor executes the machine-executable instructions, the meteorological monitoring network monitoring node scheduling system based on environmental perception implements the aforementioned meteorological monitoring network monitoring node scheduling method based on environmental perception.

[0012] In combination with the third aspect of the present application, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed, the aforementioned method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception is implemented.

[0013] In combination with any of the above aspects, by obtaining a set of environmental perception data in the area covered by the meteorological monitoring network, which includes the environmental status information of the monitoring node location and its own operating status information, the environment in which the monitoring node is located and its own status can be fully understood. Based on the environmental perception data set, comprehensive status assessment information including environmental adaptability characteristics and monitoring node reliability characteristics is generated, and the scheduling priority sequence is determined according to the comprehensive status assessment information, thereby achieving accurate scheduling of the monitoring nodes, ensuring that monitoring resources can be reasonably allocated in a complex and changing environment, and giving priority to ensuring the effective operation of key areas and key nodes. Activation, sleep or mobile scheduling operations are performed according to the scheduling priority sequence, which improves the flexibility and response speed of the monitoring network. Finally, the acquisition scope of the environmental perception data set and the generation rules of the comprehensive status assessment information are adjusted according to the feedback information of the monitoring node after the scheduling operation, further improving the overall performance of the meteorological monitoring network and the quality of the monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained by combining these drawings without paying any creative work.

[0015] Figure 1 A flow chart of a method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0017] The terms "first," "second," and so on, in the specification and claims of the present invention and the accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or end comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed therein, or may optionally include other steps or elements inherent to such process, method, product, or end.

[0018] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] Figure 1 The flowchart of the method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception provided by an embodiment of the present application is shown. It should be understood that in other embodiments, the order of some steps in the method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception of this embodiment can be shared based on actual needs, or some steps can be omitted or maintained. The details of the method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception include:

[0020] Step S110: Acquire an environmental perception data set of the area covered by the meteorological monitoring network, wherein the environmental perception data set includes environmental status information of the location of the monitoring node and operating status information of the monitoring node itself.

[0021] This embodiment uses a meteorological monitoring network around a city as an application scenario. Within this area, there are a large number of monitoring nodes. Each monitoring node is equipped with a corresponding sensing device. In terms of obtaining environmental status information at the location of the monitoring node, for example, a device for sensing temperature is set around monitoring node A, which continuously collects temperature information of multiple locations within a certain radius centered on monitoring node A. The temperature information of these locations together constitutes a data set related to temperature distribution. Suppose these locations are a1, a2, a3...an, and the corresponding temperature information is Ta1, Ta2, Ta3...Tan. The acquisition of humidity information is also similar. The humidity sensing device collects humidity information from multiple surrounding locations to form a data set related to humidity distribution. Suppose these locations are b1, b2, b3...bn, and the humidity information is Hb1, Hb2, Hb3...Hbn. The pressure gradient information is obtained by relying on pressure sensing equipment based on the changes in air pressure at different surrounding locations. For example, by comparing the air pressure values ​​Pc1, Pc2, Pc3...Pcn at different locations c1, c2, c3...cn, the pressure gradient related data can be calculated.

[0022] Regarding the operating status information of the monitoring node itself, taking monitoring node A as an example, the working stability information of its circuit module is obtained by monitoring the status of the circuit module during continuous operation to collect data, such as recording the number of abnormal fluctuations or failures of the circuit module, the duration of the abnormality, and other related information. The remaining capacity information of the energy storage unit is provided by the energy storage device and may be presented as the ratio of the remaining power to the total power. The signal transmission delay information is obtained by recording the time difference between the signal sent from monitoring node A to a specific receiving end. The data packet loss rate information is obtained by counting the relationship between the number of data packets lost during the data transmission process of monitoring node A and the total number of transmitted data packets over a period of time. After each monitoring node collects this environmental status information and its own operating status information through its own sensing device, it is aggregated to form a set of environmental perception data for the entire meteorological monitoring network coverage area.

[0023] Step S120: Generate comprehensive status evaluation information of the monitoring node based on the environmental perception data set, wherein the comprehensive status evaluation information includes environmental adaptability characteristics and monitoring node reliability characteristics.

[0024] Next, the monitoring node A is still taken as an example for description.

[0025] Step S121: Extract the temperature distribution information, humidity distribution information and pressure gradient information of the monitoring node location from the environmental perception data set, analyze the matching degree between the temperature distribution information and the target meteorological parameters, the correlation degree between the humidity distribution information and the monitoring task requirements, and the correlation degree between the pressure gradient information and the data acquisition accuracy, and generate environmental adaptability characteristics.

[0026] In this large urban perimeter meteorological monitoring scenario, monitoring node A extracts temperature distribution information, such as Ta1, Ta2, Ta3, ..., Tan, as mentioned previously. The target meteorological parameter is a set of pre-defined temperature values ​​based on meteorological research or monitoring tasks, designated as Ttarget1, Ttarget2, Ttarget3, ..., Ttargetn. To analyze the degree of match between the temperature distribution information and the target meteorological parameters, each Ta value is compared with the corresponding Ttarget value. For example, for Ta1 and Ttarget1, determine whether the difference between them is within a certain allowable range. If the difference is small, the temperature distribution at that location closely matches the target meteorological parameters; if the difference is large, the match is poor. After performing this comparative analysis for all locations, a comprehensive estimate of the degree of match between the temperature distribution information and the target meteorological parameters is obtained.

[0027] Analyze the correlation between humidity distribution information Hb1, Hb2, Hb3, ..., Hbn, and monitoring task requirements. Assume that monitoring tasks have different humidity requirements for different locations, designated Htask1, Htask2, Htask3, ..., Htaskn. Similarly, compare each Hb value with the corresponding Htask value, for example, Hb1 and Htask1, to examine their proximity. The greater the proximity, the higher the correlation between the humidity distribution at that location and the monitoring task requirements. A comprehensive analysis of all locations reveals the correlation between humidity distribution information and monitoring task requirements.

[0028] Regarding pressure gradient information, the correlation between the pressure gradients calculated from the previously acquired pressure values ​​Pc1, Pc2, Pc3, ..., Pcn at different locations c1, c2, c3, ..., cn, and data acquisition accuracy is analyzed. Data acquisition accuracy requires the pressure gradient to be within a certain reasonable range. For example, the calculated pressure gradient is analyzed to see if it is within this range. If it is, the correlation with data acquisition accuracy is good; if it is outside the range, the correlation is poor. The analysis results of temperature, humidity, and pressure gradient are combined to generate the environmental adaptability characteristics of monitoring node A.

[0029] Step S122: Extract the circuit module working stability information, energy storage unit remaining capacity information, signal transmission delay information and data packet loss rate information of the monitoring node from the environmental perception data set, evaluate the continuity of the circuit module working stability information, the sufficiency of the energy storage unit remaining capacity information, the acceptability of the signal transmission delay information, and the controllability of the data packet loss rate information, and generate the reliability characteristics of the monitoring node.

[0030] For monitoring node A, its circuit module operational stability information records the number of abnormal fluctuations or failures, the duration of abnormalities, and other information. When evaluating the degree of persistence, if the number of abnormalities within a period is small and the duration of each abnormality is short, the circuit module operational stability is high; otherwise, it is low.

[0031] The remaining capacity of the energy storage unit is presented as the ratio of remaining power to total power. When assessing the remaining capacity, a high remaining power ratio, such as above a certain percentage, indicates a high level of remaining capacity. A low remaining power ratio indicates a low level of remaining capacity.

[0032] Signal transmission delay information is the time difference between a signal being sent from monitoring node A and reaching a specific receiving end. When evaluating the acceptability, this time difference is compared with a preset acceptable delay time. If the actual delay time is less than or equal to the acceptable delay time, the signal transmission delay is considered acceptable; otherwise, it is considered acceptable.

[0033] The packet loss rate is the ratio of the number of lost packets transmitted by monitoring node A over a period of time to the total number of transmitted packets. When evaluating controllability, if the packet loss rate is below a certain set ratio, the controllability is high; otherwise, it is low. The combined results of these four aspects are used to generate the monitoring node reliability characteristics for monitoring node A.

[0034] Step S123: perform feature dimension alignment processing on the environmental adaptability feature and the monitoring node reliability feature, convert the original dimension of the environmental adaptability feature into a standardized value through linear transformation, and convert the original dimension of the monitoring node reliability feature into a standardized value through nonlinear mapping.

[0035] First, determine that the original dimension type of the environmental adaptability characteristics is a physical dimension, such as temperature dimension, humidity dimension, and air pressure dimension. For temperature distribution information, its original dimension is a temperature-related unit, the original dimension of humidity distribution information is a humidity-related unit, and the original dimension of pressure gradient information is a pressure-related unit. A linear transformation function is established for these physical dimensions. The function input is the original physical dimension value, such as the Ta value in the temperature distribution information, the Hb value in the humidity distribution information, the related calculated value in the pressure gradient information, etc., and the output is a standardized value between 0 and 1. For example, for the temperature value Ta1, through this linear transformation function, based on factors such as the relative size of Ta1 among all temperature values, a standardized value between 0 and 1 is output, set as NTa1. Similarly, the humidity and air pressure related values ​​are converted to obtain the corresponding standardized values ​​NHb1, NP1, etc.

[0036] The original dimensional types used to determine the reliability characteristics of monitoring nodes are performance dimensions, including stability, capacity, delay, and packet loss rate. A nonlinear mapping function is established for these performance dimensions. This function takes as input the raw performance dimension values, such as the number of anomalies in the circuit module's operational stability information, the remaining capacity ratio of the energy storage unit, signal transmission delay time, and data packet loss rate, and outputs a normalized value between 0 and 1. This nonlinear mapping function is trained using historical data. The training process determines the relationship between the performance dimension values ​​in the historical data and the actual monitoring node reliability performance, so that better performance dimension values ​​correspond to larger normalized values. For example, if the number of anomalies in the circuit module's operational stability information is low, the nonlinear mapping function processes the result, resulting in a relatively large normalized value between 0 and 1, designated as NS1. The same processing is performed for the remaining capacity of the energy storage unit, signal transmission delay, and data packet loss rate information, resulting in corresponding normalized values ​​such as NC1, ND1, and NL1. The standardized values ​​of environmental adaptability features such as NTa1, NHb1, NP1, etc. and the standardized values ​​of monitoring node reliability features such as NS1, NC1, ND1, NL1, etc. are used as the feature values ​​after dimension alignment to complete the feature dimension alignment process.

[0037] Step S124: determining the influence weight of the environmental adaptability feature in the meteorological data collection task and the influence weight of the monitoring node reliability feature in the stable operation of the network, wherein the influence weight is determined by the statistical analysis result of the historical scheduling data.

[0038] A large amount of historical dispatch data is collected within this large-scale peri-urban meteorological monitoring network. To determine the weight of the impact of environmental adaptability characteristics on meteorological data collection tasks, the historical dispatch data is analyzed to examine the differences in the quality and accuracy of meteorological data collection tasks when the environmental adaptability characteristics exhibit different performance. For example, in certain historical dispatches, when the temperature distribution in the environmental adaptability characteristics closely matches the target meteorological parameters, the meteorological data collection task performs better in terms of the accuracy of temperature-related data. By statistically analyzing the large amount of historical dispatch data described above, the weight of the impact of environmental adaptability characteristics on meteorological data collection tasks is determined, which is set as Wenvironment.

[0039] To determine the weight of the impact of monitoring node reliability characteristics on network stability, we similarly analyzed historical scheduling data to examine the effects of different monitoring node reliability characteristics on network stability, such as signal transmission stability and data transmission integrity. For example, in some historical scheduling scenarios, when the circuit module operating stability of the monitoring node reliability characteristic was high, network signal transmission was more stable and the packet loss rate was lower. By statistically analyzing a large amount of similar historical scheduling data, we determined the weight of the impact of monitoring node reliability characteristics on network stability, which we set as W reliable.

[0040] Step S125: Associating and combining the standardized values ​​of the environmental adaptability characteristics, the standardized values ​​of the monitoring node reliability characteristics and their corresponding influence weights to generate comprehensive status evaluation information of the monitoring node.

[0041] Taking monitoring node A as an example, the environmental adaptability characteristics are converted into a series of standardized values, such as NTa1, NHb1, NP1, etc., through the previous steps. The reliability characteristics of the monitoring node are also converted into a series of standardized values, such as NS1, NC1, ND1, NL1, etc., as well as the corresponding influence weights Wenvironment and Wreliability. The standardized values ​​of the environmental adaptability characteristics are associated with Wenvironment in a certain way. For example, NTa1, NHb1, NP1, etc. are weighted according to their importance in the environmental adaptability characteristics, and the result after processing is set as Eenvironment. Similarly, the standardized values ​​of the reliability characteristics of the monitoring node, such as NS1, NC1, ND1, NL1, etc., are weighted according to their importance in the reliability characteristics of the monitoring node and Wreliability, and the result is set as Ereliability. Finally, Eenvironment and Ereliability are combined, for example, in some splicing method, to obtain the comprehensive status assessment information of monitoring node A.

[0042] Step S130: Determine the scheduling priority sequence of the monitoring node according to the comprehensive status evaluation information.

[0043] Take monitoring node A and many other monitoring nodes as an example.

[0044] Step S131: extracting the standardized values ​​of the environmental adaptability characteristics, the standardized values ​​of the monitoring node reliability characteristics and their corresponding influence weights from the comprehensive status assessment information.

[0045] For monitoring node A, the standardized values ​​of environmental adaptability characteristics, such as NTa1, NHb1, NP1, etc., and the standardized values ​​of monitoring node reliability characteristics, such as NS1, NC1, ND1, NL1, etc., as well as the corresponding impact weights Wenvironment and Wreliable, are extracted from the comprehensive status evaluation information generated previously.

[0046] Step S132: Construct a priority calculation model, the input parameters of the priority calculation model are the standardized value of the environmental adaptability characteristics, the standardized value of the monitoring node reliability characteristics, the environmental adaptability impact weight and the monitoring node reliability impact weight, and the calculation rule of the priority calculation model is that the priority score value is equal to the product of the standardized value of the environmental adaptability characteristics and the environmental adaptability impact weight plus the product of the standardized value of the monitoring node reliability characteristics and the monitoring node reliability impact weight.

[0047] Define the model input parameters. Set the standardized values ​​of the environmental adaptability characteristics to X1, X2, X3, ..., Xn (corresponding to NTa1, NHb1, NP1, etc. of monitoring node A above). Set the standardized values ​​of the monitoring node reliability characteristics to Y1, Y2, Y3, ..., Yn (corresponding to NS1, NC1, ND1, NL1, etc.). Set the environmental adaptability impact weight to Wx (i.e., W environment), and the monitoring node reliability impact weight to Wy (i.e., W reliability). Define the model output parameter as the priority score value S. Set the calculation rules for the priority calculation model: S equals the product of X1 and Wx plus the product of X2 and Wx, ..., and so on, up to the product of Xn and Wx, plus the product of Y1 and Wy plus the product of Y2 and Wy, ..., and so on, up to the product of Yn and Wy.

[0048] Step S133: Calculate the priority score value of each monitoring node using the priority calculation model.

[0049] For monitoring node A, the extracted standardized values ​​of environmental adaptability characteristics (X1, X2, X3, ..., Xn), standardized values ​​of monitoring node reliability characteristics (Y1, Y2, Y3, ..., Yn), environmental adaptability impact weight (Wx), and monitoring node reliability impact weight (Wy) are substituted into the priority calculation model. According to the previously established calculation rules, first calculate the product of X1 and Wx, then the product of X2 and Wx, ..., and finally the product of Xn and Wx. These products are summed to obtain a value, set as V1. Next, calculate the product of Y1 and Wy, then the product of Y2 and Wy, ..., and finally the product of Yn and Wy. These products are summed to obtain another value, set as V2. Finally, V1 and V2 are added to obtain the priority score S for monitoring node A. The priority calculation model is used to calculate the priority score for each other monitoring node in the meteorological monitoring network in the same manner.

[0050] Step S134: sorting the priority scores of all monitoring nodes to generate a scheduling priority sequence arranged in order of priority score values.

[0051] Collect the priority scores calculated for all monitoring nodes. For example, there are monitoring nodes A, B, C...Z, and their respective priority scores are SA, SB, SC...SZ. Compare these priority scores, first comparing SA and SB. If SA is greater than SB, monitor node A is ranked first; if SA is less than SB, monitor node B is ranked first. According to the above method, the priority scores of all monitoring nodes are compared in pairs, and finally a scheduling priority sequence is generated, which is arranged in order of priority score from large to small (or small to large, depending on the specific needs).

[0052] Step S140: Execute activation, sleep or mobility scheduling operations of the monitoring node according to the scheduling priority sequence.

[0053] The following is an example of a monitoring node in a scheduling priority sequence.

[0054] Step S141: Extract the monitoring nodes that need to keep running from the scheduling priority sequence, the monitoring nodes that need to keep running are monitoring nodes whose priority score values ​​meet the data collection task requirements, set the monitoring nodes that need to keep running to an activated state, and maintain the continuous operation of their meteorological data collection modules and data transmission modules.

[0055] Assuming that in the scheduling priority sequence, the priority score value of the monitoring node M meets the demand of the data collection task after judgment. The judgment process is to compare the priority score value of the monitoring node M with the pre-set score threshold of the data collection task demand. If it is greater than or equal to the threshold, it is considered to meet the demand. For the monitoring node M, it is set to an active state, which means starting and maintaining the operation of its meteorological data collection module, such as the continuous operation of the data collection equipment of temperature, humidity, air pressure, etc. At the same time, the operation of the data transmission module is maintained to ensure that the collected data can be transmitted to the data processing center in time and accurately.

[0056] Step S142: Extract the monitoring node that can suspend operation from the scheduling priority sequence, which is the monitoring node whose priority score value does not affect the overall performance of the network. Set the monitoring node that can suspend operation to a sleep state and turn off the power supply of its unnecessary function modules.

[0057] In the scheduling priority sequence, the priority score value of the monitoring node N is analyzed and compared with the pre-set network overall performance influence threshold. If it is less than the threshold, it is considered that its priority score value does not affect the overall performance of the network, that is, the monitoring node can suspend operation. For the monitoring node N, it is set to a sleep state and the power supply of its unnecessary function modules is turned off, such as turning off the power supply of some sensor equipment used for auxiliary functions but not necessary in the current state to achieve the purpose of energy saving, while retaining the basic function modules for maintaining the system state in a low-power consumption operation state.

[0058] Step S143: Extract the monitoring node whose position needs to be adjusted from the scheduling priority sequence, which is the monitoring node whose deployment position does not match the current meteorological element change area. According to the pressure gradient information change trend and the temperature distribution information change trend in the environmental adaptability feature, the target deployment position is determined.

[0059] In the scheduling priority sequence, the monitoring node P judges that its deployment position does not match the current meteorological element change area. The judgment is made by comparing the barometric gradient information and temperature distribution information in the environmental adaptability characteristics of the monitoring node P with the meteorological element change of the surrounding area. If it is found that the barometric gradient change and temperature distribution change of the position are quite different from the overall meteorological element change trend, it is considered that the deployment position does not match. For the monitoring node P, the barometric gradient information change trend in the environmental adaptability characteristics is extracted, the barometric gradient change of different position points in a period of time is analyzed, for example, by comparing the barometric gradient values of different position points at different times, the barometric gradient change rate is judged. The area with a barometric gradient change rate greater than the set change rate is identified as a barometric sensitive area. At the same time, the temperature distribution information change trend is extracted, the temperature change rate is analyzed by comparing the temperature values of different position points at different times, the area with a temperature change rate greater than the set rate is identified as a temperature sensitive area. Then the spatial overlap of the barometric sensitive area and the temperature sensitive area is analyzed to determine the core area of meteorological element change.

[0060] Step S1431: Calculate the geometric center coordinates of the core area as the target deployment position.

[0061] For the determined core area of meteorological element change, assuming that the area is composed of multiple position points, the geometric center coordinates of the area are calculated by certain geometric calculation methods, such as comprehensive calculation of the coordinates of these position points. The geometric center coordinates are the target deployment position of the monitoring node P.

[0062] Step S1432: Verify the matching degree of the target deployment position with the current meteorological monitoring task requirement, so that the target deployment position covers the main meteorological element change area.

[0063] The target deployment position of the monitoring node P calculated is verified with the current meteorological monitoring task requirement. Whether the target deployment position can cover the main meteorological element change area is analyzed, for example, whether the position is in the core area where the barometric sensitive area and the temperature sensitive area overlap, or is close to the core area and can effectively monitor the change of the main meteorological element. If it meets the requirements, it is considered that the target deployment position matches the current meteorological monitoring task requirement; if it does not meet the requirements, the analysis process of the barometric gradient information change trend and the temperature distribution information change trend is re-considered, whether the identification of the barometric sensitive area and the temperature sensitive area is accurate, and whether the determination of the core area is reasonable are checked, the target deployment position is calculated again and verified until the target deployment position can cover the main meteorological element change area and meet the current meteorological monitoring task requirement.

[0064] Step S144: generating a moving path from the current deployment position to the target deployment position, and controlling the monitoring nodes whose positions need to be adjusted to perform position adjustment operations according to the moving path.

[0065] For monitoring node P, after determining the target deployment location, it is necessary to plan a movement path from its current deployment location to the target deployment location. First, obtain the coordinate information of the current location of monitoring node P, set as (X current, Y current), and the coordinate information of the target deployment location, set as (X target, Y target). At the same time, consider environmental factors during the movement of the monitoring node, such as whether there are obstacles in the movement area of ​​the monitoring node. Assume that the movement area is divided into multiple grid areas, each grid area has corresponding attribute information, such as whether it is passable.

[0066] Starting from the current grid, a path search algorithm is used to find a path to the target grid. This path search algorithm can be a breadth-first search or other algorithm suitable for the scenario. For example, starting from the starting grid, adjacent, traversable grids are added to the search queue. Grids are then removed from the queue one by one for inspection. If the removed grid is the target grid, a path has been found. If not, adjacent, traversable, unvisited grids are added to the queue. This process continues until the target grid is found or it is determined that no path exists.

[0067] During the search process, the predecessor grids of each visited grid are recorded. Once the target grid is found, a path can be generated from the starting point to the target grid by backtracking through the predecessor grids. For example, if the target grid's predecessor is grid A, A's predecessor is grid B, and B's predecessor is the starting grid, then the path is from the starting grid through grid B, A, and finally to the target grid. The generated path consists of a series of grid locations that correspond to locations in real-world geographic space, forming a movement path from the current deployment location to the target deployment location.

[0068] The monitoring node P is controlled to perform position adjustment operations according to the generated movement path. The monitoring node P may be equipped with a mobile device, such as a mobile chassis with wheels or a device with flight capabilities. Based on the order of grid positions in the movement path, the mobile control module of the monitoring node P receives the coordinate information of each grid position and converts it into control instructions that can be recognized by the mobile device. For example, if it is a wheeled mobile chassis, the control instructions may include operation instructions such as forward, backward, and turning, as well as corresponding parameters such as speed and angle. According to these control instructions, the monitoring node P moves to each grid position in the path in sequence, and finally reaches the target deployment position, completing the position adjustment operation.

[0069] Step S150: adjusting the acquisition scope of the environmental perception data set and the generation rule of the comprehensive status evaluation information according to the feedback information of the monitoring nodes after the scheduling operation.

[0070] After the scheduling operation is completed, multiple monitoring nodes such as monitoring nodes A, B, and C are taken as an example for explanation.

[0071] Step S151: Collect the real-time meteorological monitoring data fed back by the activated state monitoring node and the energy consumption optimization data fed back by the dormant state monitoring node as monitoring node feedback information, wherein the real-time meteorological monitoring data includes temperature measurement values, humidity measurement values ​​and air pressure measurement values, and the energy consumption optimization data includes energy consumption rate information in dormant mode.

[0072] Active monitoring node A continuously provides real-time meteorological data. Temperature measurements are obtained using its own temperature sensor. At regular intervals, the sensor measures the temperatures of multiple locations within a certain range centered on monitoring node A, forming a set of temperature measurements. These values ​​are Tmeasure1, Tmeasure2, Tmeasure3, ..., Tmeasuren. Humidity measurements are obtained using a humidity sensor. Similarly, humidity measurements are taken at multiple locations within the specified range, forming a set of humidity measurements, Hmeasure1, Hmeasure2, Hmeasure3, ..., Hmeasuren. Air pressure measurements are obtained using a pressure sensor, forming a set of air pressure measurements, Pmeasure1, Pmeasure2, Pmeasure3, ..., Pmeasuren.

[0073] For monitoring nodes B in sleep mode, the feedback energy consumption optimization data is primarily information about the energy consumption rate in sleep mode. This information is obtained by the energy monitoring device within monitoring node B. The energy monitoring device continuously monitors the power consumption of the energy storage unit in sleep mode and calculates the energy consumption rate per unit time, which is set as E rate.

[0074] The real-time meteorological monitoring data fed back by all active state monitoring nodes and the energy consumption optimization data fed back by dormant state monitoring nodes are aggregated to form monitoring node feedback information.

[0075] Step S152: Analyze the spatial coverage of the real-time meteorological monitoring data and identify areas that are not fully covered as monitoring blind spots.

[0076] Taking aggregated real-time meteorological monitoring data as an example, assume that the entire meteorological monitoring network coverage area is divided into multiple spatial units, each numbered, such as Unit 1, Unit 2, Unit 3, and so on. For temperature measurements, analyze whether there are a sufficient number of measurement points within each unit. If the number of temperature measurement points within a unit falls below a pre-defined standard, or if the distribution of measurement points is too sparse to accurately reflect temperature fluctuations in the area, the unit is considered to be under-covered in terms of temperature measurement. A similar analysis is performed for humidity and air pressure measurements.

[0077] Taking into account the analysis results of temperature, humidity, and air pressure measurements, we identify those areas where coverage for multiple meteorological elements is insufficient and combine them into monitoring blind spots. For example, if area 5 has insufficient or irrational measurement points for temperature, humidity, and air pressure, it is identified as a monitoring blind spot.

[0078] Step S153: If there is a monitoring blind spot, expand the environmental status information collection range of the location corresponding to the monitoring blind spot in the environmental perception data set, and increase the atmospheric physical property information collection frequency of the monitoring nodes in the monitoring blind spot.

[0079] When a blind spot is identified, such as area unit 5, the environmental status information collection range for the corresponding location in area unit 5 is expanded within the environmental perception data set. While environmental status information collection may have originally been based on the edge of the blind spot, the collection range is now expanded a certain distance into the blind spot. For example, if the collection range was originally a circular area with a radius of R1 around the edge of the blind spot, the radius is now expanded to R2 (R2 is larger than R1), thereby covering more locations within the blind spot.

[0080] For monitoring nodes in blind spots, such as monitoring node C, the frequency of atmospheric physical property information collection is increased. Originally, monitoring node C might collect atmospheric physical property information such as temperature, humidity, and air pressure every time interval T1. Now, this collection interval is shortened to T2 (T2 is less than T1). This allows more atmospheric physical property information about the blind spot to be collected per unit time, allowing for a more accurate understanding of the meteorological conditions in the blind spot.

[0081] Step S154: Analyze the correlation between the energy-saving effect of the energy consumption optimization data and the reliability characteristics of the monitoring node, and evaluate whether the weight distribution of the circuit module working stability information and the energy storage unit remaining capacity information in the generation rule of the monitoring node reliability characteristics is reasonable.

[0082] Taking the energy consumption rate E (E-rate) in the energy optimization data fed back by sleep-mode monitoring node B as an example, we analyze its correlation with the monitoring node reliability characteristics. A lower E-rate indicates better energy conservation in sleep mode. Combined with the monitoring node reliability characteristics of monitoring node B, the circuit module operating stability information records the stability of the circuit module before, during, and after sleep, such as whether abnormal fluctuations or failures occur; and the energy storage unit remaining capacity information records the changes in the proportion of the remaining power of the energy storage unit to the total power in sleep mode.

[0083] When evaluating the rationality of the weight distribution between circuit module operational stability information and energy storage unit remaining capacity information in the monitoring node reliability feature generation rules, consider that if the energy-saving effect is good (low E rate), the circuit module operational stability information performs well, but the weight of the energy storage unit remaining capacity information is too high, resulting in an unsatisfactory monitoring node reliability feature evaluation result, which may indicate an unreasonable weight distribution. Conversely, if the energy storage unit remaining capacity information performs well, but the weight of the circuit module operational stability information is too low, the evaluation result may also not accurately reflect the actual reliability of the monitoring node.

[0084] By comprehensively analyzing the relationship between the energy consumption optimization data of multiple dormant monitoring nodes and the reliability characteristics of the monitoring nodes, we can determine the contribution of the circuit module working stability information and the energy storage unit remaining capacity information to the monitoring node reliability characteristic evaluation under different energy-saving effects, and thus evaluate whether the weight distribution is reasonable.

[0085] Step S155: adjusting the weight values ​​of the circuit module working stability information and the energy storage unit remaining capacity information in the monitoring node reliability feature generation rule according to the evaluation result, and updating the generation rule of the comprehensive status evaluation information.

[0086] Assume that the evaluation in step S154 reveals that the weight of the circuit module operational stability information is too low when evaluating the reliability characteristics of the monitoring node, while the weight of the energy storage unit remaining capacity information is too high. Based on the evaluation results, the weight of the circuit module operational stability information is appropriately increased, with the original weight being W circuit original adjusted to W circuit new (W circuit new is greater than W circuit original). Simultaneously, the weight of the energy storage unit remaining capacity information is appropriately decreased, with the original weight being W capacity original adjusted to W capacity new (W capacity new is less than W capacity original).

[0087] When updating the rules for generating comprehensive status assessment information, since the monitoring node reliability characteristics are part of the comprehensive status assessment information, changes to the rules for generating the monitoring node reliability characteristics will affect the generation of the comprehensive status assessment information. In the process of generating the comprehensive status assessment information by associating and combining the environmental adaptability characteristics and the monitoring node reliability characteristics, the circuit module operating stability information and the energy storage unit remaining capacity information are reweighted according to the adjusted weight values ​​and then combined with other monitoring node reliability characteristics and the environmental adaptability characteristics. This updates the rules for generating the comprehensive status assessment information, allowing the comprehensive status assessment information to more accurately reflect the actual status of the monitoring node.

[0088] In the above embodiments, the environmental perception-based meteorological monitoring network monitoring node scheduling system for executing the above method embodiments has at least one processor, a control module (chip set) coupled to at least one of the (at least one) processors, a memory coupled to the control module, a non-volatile memory (NVM) / storage device coupled to the control module, at least one load / output device coupled to the control module, and a network interface coupled to the control module.

[0089] The processor may include at least one single-core or multi-core processor, and the processor may include any combination of general-purpose processors or dedicated processors (such as graphics processors, application processors, baseband processors, etc.). For some alternative implementations, the meteorological monitoring network monitoring node scheduling system based on environmental perception can be based on electronic devices such as the gateway described in the embodiments of this application.

[0090] For some alternative embodiments, the environmental awareness-based meteorological monitoring network monitoring node scheduling system may include at least one computer-readable medium (e.g., a memory or NVM / storage device) having instructions and at least one processor integrated with the at least one computer-readable medium and configured to execute instructions to implement a module to perform the actions described in the present disclosure.

[0091] For one embodiment, the control module may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) and / or any suitable device or component in communication with the control module.

[0092] The control module may include a memory controller module to provide an interface to the memory. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0093] The memory may be used, for example, to load and store data and / or instructions for the environment-awareness-based meteorological monitoring network monitoring node scheduling system. For one embodiment, the memory may include any suitable volatile memory, such as a suitable DRAM.

[0094] For one embodiment, the control module may include at least one load / output controller to provide an interface to the NVM / storage device and the (at least one) load / output device.

[0095] For example, NVM / storage devices may be used to store data and / or instructions. The NVM / storage devices may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (at least one) non-volatile storage device (e.g., at least one hard disk drive (HDD), at least one compact disc (CD) drive, and / or at least one digital versatile disc (DVD) drive).

[0096] The NVM / storage device may include storage resources that are physically part of the device on which the environment-aware weather monitoring network monitoring node scheduling system is installed, or may be accessible to the device without being part of the device. For example, the NVM / storage device may be accessed via (at least one) load / output device over a network.

[0097] The (at least one) load-in / output device may provide an interface for the meteorological monitoring network monitoring node scheduling system based on environmental awareness to communicate with any other appropriate device, and the load-in / output device may include a communication component, a phonetic component, a sensor component, etc. The network interface may provide an interface for the meteorological monitoring network monitoring node scheduling system based on environmental awareness to communicate based on at least one network, and the meteorological monitoring network monitoring node scheduling system based on environmental awareness may wirelessly communicate with at least one component of a wireless network based on any of at least one wireless network priors and / or protocols, for example, access a wireless network based on communication priors.

[0098] For one embodiment, at least one of the (at least one) processors may be loaded together with the logic of at least one controller of a control module (e.g., a memory controller module). For one embodiment, at least one of the (at least one) processors may be loaded together with the logic of at least one controller of a control module to form a system-level load. For one embodiment, at least one of the (at least one) processors may be fused on the same die with the logic of at least one controller of a control module. For one embodiment, at least one of the (at least one) processors may be fused on the same die with the logic of at least one controller of a control module to form a system-on-chip (SoC).

[0099] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0100] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception described in the aforementioned embodiment.

[0101] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the environmental perception-based meteorological monitoring network monitoring node scheduling method described in the aforementioned embodiment.

[0102] The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple modules. Some or all of the modules may be selected based on actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement the present invention without inventive effort.

[0103] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, the storage medium including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of storing or storing data.

[0104] Finally, it should be noted that what is disclosed above is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception, characterized in that: The method comprises: Acquire an environmental perception data set for an area covered by a meteorological monitoring network, wherein the environmental perception data set includes environmental status information of a location where a monitoring node is located and operating status information of the monitoring node itself; Generate comprehensive status assessment information of the monitoring node based on the environmental perception data set, wherein the comprehensive status assessment information includes environmental adaptability characteristics and monitoring node reliability characteristics; Determining a scheduling priority sequence of monitoring nodes based on the comprehensive status evaluation information; Executing activation, sleep or mobile scheduling operations of monitoring nodes according to the scheduling priority sequence; The acquisition scope of the environmental perception data set and the generation rule of the comprehensive status evaluation information are adjusted according to the feedback information of the monitoring nodes after the scheduling operation.

2. The method according to claim 1, characterized in that The generating of comprehensive status assessment information of the monitoring node based on the environmental perception data set includes: Extracting temperature distribution information, humidity distribution information, and pressure gradient information at the location of the monitoring node from the environmental perception data set, analyzing the degree of matching between the temperature distribution information and target meteorological parameters, the degree of correlation between the humidity distribution information and monitoring task requirements, and the degree of correlation between the pressure gradient information and data acquisition accuracy, to generate environmental adaptability features; Extracting circuit module working stability information, energy storage unit remaining capacity information, signal transmission delay information, and data packet loss rate information of the monitoring node from the environmental perception data set, evaluating the persistence of the circuit module working stability information, the sufficiency of the energy storage unit remaining capacity information, the acceptability of the signal transmission delay information, and the controllability of the data packet loss rate information, and generating a reliability feature of the monitoring node; Performing feature dimension alignment processing on the environmental adaptability feature and the monitoring node reliability feature, converting the original dimension of the environmental adaptability feature into a standardized value through linear transformation, and converting the original dimension of the monitoring node reliability feature into a standardized value through nonlinear mapping; Determining the influence weight of the environmental adaptability feature in the meteorological data collection task and the influence weight of the monitoring node reliability feature in the stable operation of the network, wherein the influence weight is determined based on the statistical analysis results of historical scheduling data; The standardized values ​​of the environmental adaptability characteristics, the standardized values ​​of the monitoring node reliability characteristics and their corresponding influence weights are associated and combined to generate comprehensive status evaluation information of the monitoring node.

3. The method according to claim 2, characterized in that The step of performing feature dimension alignment processing on the environmental adaptability feature and the monitoring node reliability feature, converting the original dimension of the environmental adaptability feature into a standardized value through linear transformation, and converting the original dimension of the monitoring node reliability feature into a standardized value through nonlinear mapping, includes: Determining that the original dimension type of the environmental adaptability feature is a physical dimension, wherein the physical dimension includes a temperature dimension, a humidity dimension, and an air pressure dimension; Determining that the original dimension type of the reliability characteristic of the monitoring node is a performance dimension, wherein the performance dimension includes a stability dimension, a capacity dimension, a delay dimension, and a packet loss rate dimension; Establishing a linear transformation function for the physical dimension of the environmental adaptability characteristic, wherein the input of the linear transformation function is the original physical dimension value and the output is a standardized value between 0 and 1; Establishing a nonlinear mapping function for the performance dimension of the reliability characteristic of the monitoring node, wherein the input of the nonlinear mapping function is the original performance dimension value and the output is a normalized value between 0 and 1, and the nonlinear mapping function is trained by historical data so that the better the performance dimension value, the larger the corresponding normalized value; Applying the linear transformation function to convert the original physical dimension value of the environmental adaptability characteristic to obtain a standardized environmental adaptability value; Applying the nonlinear mapping function to convert the original performance dimension value of the monitoring node reliability feature to obtain a standardized value of the monitoring node reliability; The standardized value of the environmental adaptability and the standardized value of the monitoring node reliability are used as the feature values ​​after dimension alignment to complete the feature dimension alignment process.

4. The method according to claim 1, wherein The determining of the scheduling priority sequence of the monitoring nodes according to the comprehensive status evaluation information includes: Extracting the standardized values ​​of the environmental adaptability characteristics, the standardized values ​​of the monitoring node reliability characteristics and their corresponding impact weights from the comprehensive status assessment information; Constructing a priority calculation model, wherein the input parameters of the priority calculation model are the standardized value of the environmental adaptability feature, the standardized value of the monitoring node reliability feature, the environmental adaptability impact weight, and the monitoring node reliability impact weight, and the calculation rule of the priority calculation model is that the priority score value is equal to the product of the standardized value of the environmental adaptability feature and the environmental adaptability impact weight plus the product of the standardized value of the monitoring node reliability feature and the monitoring node reliability impact weight; Calculate the priority score value of each monitoring node using the priority calculation model; The priority scores of all monitoring nodes are sorted to generate a scheduling priority sequence arranged in order of priority score values.

5. The method according to claim 4, characterized in that The constructing of the priority calculation model includes: The model input parameters are defined as the standardized value X of the environmental adaptability characteristic, the standardized value Y of the monitoring node reliability characteristic, the environmental adaptability impact weight Wx, and the monitoring node reliability impact weight Wy; Define the model output parameter as the priority score value S; Set the calculation rules of the priority calculation model; The calculation rules are verified by historical scheduling data, and multiple groups of historical environmental adaptability characteristic standardized values, monitoring node reliability characteristic standardized values ​​and their corresponding impact weights are selected to calculate the historical priority score value; Comparing the matching degree between the historical priority score value and the actual scheduling effect, and adjusting the calculation rule; The input parameters, output parameters and calculation rules are combined to generate a priority calculation model.

6. The method according to claim 1, characterized in that The performing the activation, sleep or mobility scheduling operation of the monitoring node according to the scheduling priority sequence includes: Extracting monitoring nodes that need to be kept running from the scheduling priority sequence, wherein the monitoring nodes that need to be kept running are monitoring nodes whose priority scores meet the requirements of the data collection task, setting the monitoring nodes that need to be kept running to an activated state, and maintaining the continuous operation of their meteorological data collection modules and data transmission modules; Extracting monitoring nodes that can be suspended from the scheduling priority sequence, wherein the monitoring nodes that can be suspended are monitoring nodes whose priority scores do not affect the overall performance of the network, setting the monitoring nodes that can be suspended to a dormant state, and shutting down power to non-essential functional modules thereof; Extracting monitoring nodes whose positions need to be adjusted from the scheduling priority sequence, wherein the monitoring nodes whose deployment locations do not match the current meteorological element change area, and determining target deployment locations based on pressure gradient information change trends and temperature distribution information change trends in the environmental adaptability characteristics; A moving path from the current deployment position to the target deployment position is generated, and the monitoring nodes whose positions need to be adjusted are controlled to perform position adjustment operations according to the moving path.

7. The method according to claim 6, characterized in that The determining of the target deployment position according to the pressure gradient information change trend and the temperature distribution information change trend in the environmental adaptability characteristics includes: Extracting the pressure gradient information change trend in the environmental adaptability feature, and identifying areas where the pressure gradient change rate is greater than a set change rate as pressure-sensitive areas; Extracting the temperature distribution information change trend in the environmental adaptability feature, and identifying areas where the temperature change rate is greater than a set rate as temperature sensitive areas; Analyze the spatial overlap between the pressure sensitive area and the temperature sensitive area to determine the core area of ​​meteorological element changes; Calculating the geometric center coordinates of the core area as the target deployment position; Verify the degree of matching between the target deployment location and the current meteorological monitoring task requirements so that the target deployment location covers the area where major meteorological elements change.

8. The method according to claim 1, characterized in that The adjusting the acquisition scope of the environmental perception data set and the generation rule of the comprehensive status assessment information according to the feedback information of the monitoring nodes after the scheduling operation includes: Collecting real-time meteorological monitoring data fed back by active state monitoring nodes and energy consumption optimization data fed back by dormant state monitoring nodes as monitoring node feedback information, wherein the real-time meteorological monitoring data includes temperature measurement values, humidity measurement values, and air pressure measurement values, and the energy consumption optimization data includes energy consumption rate information in dormant mode; Analyzing the spatial coverage of the real-time meteorological monitoring data and identifying insufficiently covered areas as monitoring blind spots; If there is a monitoring blind spot, expand the environmental status information collection range corresponding to the monitoring blind spot in the environmental perception data set, and increase the frequency of atmospheric physical property information collection of the monitoring nodes in the monitoring blind spot; Analyze the correlation between the energy-saving effect of the energy consumption optimization data and the reliability characteristics of the monitoring nodes, and evaluate whether the weight distribution of the circuit module working stability information and the energy storage unit remaining capacity information in the generation rule of the monitoring node reliability characteristics is reasonable; The weight values ​​of the circuit module working stability information and the energy storage unit remaining capacity information in the monitoring node reliability feature generation rule are adjusted according to the evaluation results, and the generation rule of the comprehensive status evaluation information is updated.

9. A meteorological monitoring network monitoring node scheduling system based on environmental perception, characterized in that: It includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a computer, the method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a computer, the method for scheduling monitoring nodes in a meteorological monitoring network based on environmental perception according to any one of claims 1 to 8 is implemented.

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