Data collection and monitoring method and system based on wireless sensor network

Abstract

The invention discloses a data collection and monitoring method and system based on a wireless sensor network, and relates to the technical field of wireless sensor networks. The method discloses the following steps: step one: collecting monitoring data of each sensor node based on a dynamic period; step two: acquiring an abnormal review value of the sensor node in real time, and judging whether the data monitored by the sensor node needs to be reviewed based on a comparison result between the abnormal review value and its threshold value; step three: reviewing the data monitored by the sensor node, and judging whether the data monitored by the sensor node needs to be eliminated. The method of the invention can timely judge whether the data collected by the sensor node needs to be reviewed, avoids the collection of erroneous data in the wireless sensor network, reviews the data collected by the sensor node, and optimizes the efficiency of the data review on the basis of ensuring the accuracy of the data review.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor networks, and more specifically, to a data collection and monitoring method and system based on wireless sensor networks. Background Art

[0002] Wireless Sensor Networks (WSN) is a distributed sensor network whose terminals are sensors that can sense and detect the outside world. Currently, wireless sensor networks monitor and collect corresponding data through each sensor node.

[0003] When the data collected by the sensor node is abnormal, the data monitoring personnel can find the data abnormality in time. However, the data abnormality may be caused by the sensor node data collection error. This data collection method will affect the data monitoring personnel's judgment of the real data. If the authenticity of the data collected by the sensor node needs to be verified, it is necessary to arrange corresponding personnel to arrive at the corresponding sensor node location for manual inspection. This data verification method is very inefficient. How to improve the verification efficiency of the collected data while ensuring the accurate collection of sensor node data is a problem that needs to be solved urgently. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention aims to provide a data collection and monitoring method and system based on a wireless sensor network.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The data collection and monitoring method based on wireless sensor network includes the following steps:

[0007] Step 1: Dynamically periodically collect monitoring data from each sensor node;

[0008] Step 2: Obtain the abnormal review value of the sensor node in real time, and determine whether the data monitored by the sensor node needs to be reviewed based on the comparison result between the abnormal review value and its threshold;

[0009] Step 3: Review the data monitored by the sensor nodes to determine whether the data monitored by the sensor nodes needs to be eliminated.

[0010] Furthermore, the data collection and monitoring system based on the wireless sensor network includes a collection and monitoring module, a review and determination module, and a data review module;

[0011] The acquisition monitoring module acquires monitoring data of each sensor node based on a dynamic period;

[0012] The review determination module is used to obtain the abnormal review value of the sensor node in real time, and determine whether the data monitored by the sensor node needs to be reviewed based on the comparison result between the abnormal review value and its threshold value;

[0013] The data review module is used to review the data monitored by the sensor node and determine whether the data monitored by the sensor node needs to be discarded.

[0014] Furthermore, the monitoring data of each sensor node is collected based on a dynamic period, specifically: based on a preset period, at each period node, the monitoring data of each sensor node is obtained, a data monitoring log is generated, the data type of the data monitoring log is obtained, and the expected data range of this type of data is obtained; when the data volume of the data monitoring log is outside the expected data range, the preset period of the sensor node is shortened.

[0015] Furthermore, the data monitoring log includes data type, data volume, monitoring time, and monitoring location.

[0016] Furthermore, the abnormality review value of the sensor node is obtained by:

[0017] Get all the data monitoring logs of the sensor node within n time periods before the current time of the system, sort all the data monitoring logs in the order of monitoring time, calculate the time difference between the two adjacent monitoring times after sorting, get the monitoring interval, sum up all the monitoring intervals and take the average, get the average monitoring interval, and mark it as TSP, mark the total number of data monitoring logs as REW, and use the formula The abnormal review value TDS of the sensor node is obtained, where a1 is the average monitoring interval coefficient and a2 is the total number coefficient of data monitoring logs.

[0018] Furthermore, based on the comparison result between the abnormal review value and its threshold, it is determined whether the data monitored by the sensor node needs to be reviewed. Specifically, an abnormal review threshold is set. When the abnormal review value of the sensor node is greater than or equal to the abnormal review threshold, the sensor node is marked as a review sensor node, the monitoring position of the review sensor node is obtained, and the remaining sensor nodes in the monitoring position are marked as co-located sensor nodes.

[0019] Further, the data monitored by the sensor node is reviewed, specifically: the monitoring data type of the review sensor node is marked as the review data type, the monitoring data type of the co-location sensor node is marked as the co-location data type, and through the knowledge graph association analysis, the co-location sensor node of the co-location data type associated with the review data type is marked as the review associated node;

[0020] Obtain the monitoring fluctuation value and data retention value of the verification sensor node and mark them as [A1 ,A 2 ], obtain the monitoring fluctuation value and data retention value of the review-related node, and mark it as [B 1 ,B 2 ], use the cosine similarity algorithm to obtain the collection linkage value of the review sensor node and the review associated node, set the collection linkage threshold, and when the collection linkage value is greater than or equal to the collection linkage threshold, mark the review associated node as a collection linkage node.

[0021] Furthermore, the monitoring fluctuation value is obtained by the following method: obtain m data monitoring logs of the sensor node before the current time of the system, sort all the data monitoring logs in the order of monitoring time, calculate the difference between two adjacent data after sorting, obtain the data fluctuation amount, set the expected fluctuation amount, when the data fluctuation amount is greater than or equal to the expected fluctuation amount, increase the number of data fluctuations by one, sum up all the data fluctuation times, obtain the total number of data fluctuations, and mark it as PWZ, and use the formula The monitoring fluctuation value FSC of the sensor node is obtained, where b1 is the total number of data fluctuation coefficients and b2 is the number of data monitoring log coefficients.

[0022] Furthermore, the data retention value is obtained by the following method: obtain all data retention records of the sensor node before the current time of the system, sort all data retention records in the order of data retention time, calculate the time difference between the data retention time of two adjacent data retention records after sorting, and obtain the data retention interval, sum up all data retention intervals and take the average to obtain the average retention interval, and mark it as GHD, mark the total number of data retention records as ERW, and use the formula The data retention value VBX of the sensor node is obtained, where c1 is the average retention interval coefficient and c2 is the total number coefficient of data retention records.

[0023] Further, it is determined whether the data monitored by the sensor node needs to be eliminated. Specifically, the total number of acquisition linkage nodes is marked as SPK, the acquisition linkage values ​​of the acquisition linkage nodes are summed and averaged to obtain the acquisition linkage mean value, which is marked as TMJ, and the total number of verification-related nodes is marked as DXC. Get the collection effective value AZY of the review sensor node, where d1 is the total number coefficient of collection linkage nodes, d2 is the collection linkage mean coefficient, and d3 is the total number coefficient of review association nodes. Set the collection effective threshold. When the collection effective value is greater than or equal to the collection effective threshold, retain the monitoring data collected by the review sensor node within n time periods before the current time of the system, and generate the data retention record of the review sensor node. The data retention record includes the data retention time.

[0024] When the collection effective value is less than the collection effective threshold, the monitoring data collected by the review sensor node within n time periods before the current time of the system will be eliminated.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The method of the present invention can timely determine whether the data collected by the sensor node needs to be reviewed, avoid the collection of erroneous data in the wireless sensor network, review the data collected by the sensor node, and optimize the efficiency of data review on the basis of ensuring the accuracy of data review;

[0027] 2. Set up a collection monitoring module and a review and judgment module to periodically collect and monitor data from each sensor node in the wireless sensor network, and dynamically adjust the data collection cycle according to the monitoring results. Set up a data review module to review and analyze the data collected by the sensor nodes through data association, and there is no need for relevant personnel to reach the sensor node location for data review and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of the present invention;

[0029] Figure 2 A flowchart for determining whether the data monitored by the sensor node needs to be reviewed;

[0030] Figure 3 A flowchart for determining whether the data monitored by the sensor node needs to be eliminated. DETAILED DESCRIPTION

[0031] Example 1

[0032] Reference Figure 1

[0033] The data collection and monitoring method based on wireless sensor network includes the following steps:

[0034] Step 1: Dynamically periodically collect monitoring data from each sensor node;

[0035] Step 2: Obtain the abnormal review value of the sensor node in real time, and determine whether the data monitored by the sensor node needs to be reviewed based on the comparison result between the abnormal review value and its threshold;

[0036] Step 3: Review the data monitored by the sensor nodes to determine whether the data monitored by the sensor nodes needs to be eliminated.

[0037] Through steps one and two, it is possible to determine in time whether the data collected by the sensor nodes needs to be reviewed, thereby avoiding the collection of erroneous data in the wireless sensor network. Through step three, the data collected by the sensor nodes can be reviewed, thereby optimizing the efficiency of data review while ensuring the accuracy of data review.

[0038] Example 2

[0039] Reference Figures 2 to 3 The data collection and monitoring system based on wireless sensor network includes a collection and monitoring module, a review and determination module, and a data review module.

[0040] Collection and monitoring module: Based on the preset cycle, at each cycle node, the monitoring data of each sensor node is obtained and a data monitoring log is generated. The data monitoring log includes the data type, data volume, monitoring time, and monitoring location (that is, the monitoring position of the sensor node. For example, when the sensor node is monitoring the electricity consumption of campus study room A, the monitoring location is campus study room A).

[0041] Get the data type of the data monitoring log, and get the expected data range of this type of data. When the data volume of the data monitoring log is within the expected data range, do not perform corresponding processing. When the data volume of the data monitoring log is outside the expected data range, shorten the preset period of the sensor node (the specific shortening of the preset period is adjusted according to actual needs. A shortening of the preset period means that the interval for the sensor node to obtain monitoring data is shortened. For example, if the monitoring data is originally obtained every 60 seconds, it can be adjusted to every 40 seconds).

[0042] For example, wireless sensor network monitoring in campus, multiple sensor nodes need to monitor various data in the campus, such as the water consumption of students for bathing (which can be accurate to the water consumption of each shower room in the campus), the electricity consumption of the campus study room (which can be accurate to the electricity consumption of each campus study room. For example, if there are four study rooms A, B, C, and D in the campus, the electricity consumption of the campus study room A, the electricity consumption of the campus study room B, the electricity consumption of the campus study room C, and the electricity consumption of the campus study room D are monitored respectively), etc. Each data corresponds to an expected data range, such as the water consumption of students for bathing corresponds to a range of students for bathing. Each expected data range can be adjusted according to actual needs.

[0043] Review and judgment module: obtain the abnormal review value of the sensor node in real time, set the abnormal review threshold, the abnormal review threshold is the system setting threshold, and can be modified according to actual needs.

[0044] When the abnormal review value of a sensor node is greater than or equal to the abnormal review threshold, the sensor node is marked as a review sensor node, the monitoring position of the review sensor node is obtained, and the remaining sensor nodes in the monitoring position are marked as co-located sensor nodes (multiple sensor nodes are set in the same monitoring position, for example: classroom A is equipped with a useful power monitoring sensor node, a noise monitoring sensor node, a temperature monitoring sensor, a brightness monitoring sensor node, etc.).

[0045] When the abnormality review value of the sensor node is less than the abnormality review threshold, no corresponding processing is performed.

[0046] The abnormal review value of the sensor node is obtained in the following way:

[0047] Get all the data monitoring logs of the sensor node within n time periods before the current time of the system, sort all the data monitoring logs in the order of monitoring time, calculate the time difference between the two adjacent monitoring times after sorting, get the monitoring interval, sum up all the monitoring intervals and take the average, get the average monitoring interval, and mark it as TSP, mark the total number of data monitoring logs as REW, and use the formula The abnormal review value TDS of the sensor node is obtained, where a1 is the average monitoring interval coefficient, a2 is the total number coefficient of data monitoring logs, the value of a1 is 0.84, and the value of a2 is 0.71.

[0048] By setting up a collection monitoring module and a review and judgment module, data can be collected and monitored periodically for each sensor node in the wireless sensor network, and the data collection cycle can be dynamically adjusted according to the monitoring results.

[0049] Data review module: Mark the monitoring data type of the review sensor node as the review data type, mark the monitoring data type of the co-location sensor node as the co-location data type, and through knowledge graph association analysis, mark the co-location sensor node of the co-location data type associated with the review data type as a review-associated node.

[0050] Different types of data are represented in the form of knowledge graphs. For example, the relationships in the knowledge graph represent the associations between different types of data, such as influence relationships, progressive relationships, etc. The knowledge graph can accurately reflect the relationships between different types of data.

[0051] For example, the electricity consumption data in the classroom is correlated with the brightness data, temperature data, etc. The greater the electricity consumption in the classroom, it may be because the lighting tubes in the classroom are always on, or it may be because the air conditioner in the classroom is always on, including but not limited to the above correlations.

[0052] Obtain the monitoring fluctuation value and data retention value of the verification sensor node and mark them as [A 1,A 2 ], obtain the monitoring fluctuation value and data retention value of the review-related node, and mark it as [B 1 ,B 2 ], use the cosine similarity algorithm to obtain the collection linkage value of the review sensor node and the review association node, set the collection linkage threshold, the collection linkage threshold is the system setting threshold, which can be modified according to actual needs. When the collection linkage value is greater than or equal to the collection linkage threshold, the review association node is marked as a collection linkage node. When the collection association value is less than the collection association threshold, no corresponding processing is performed.

[0053] If the monitoring fluctuation value and data retention value of the review sensor node are 8.5 and 12.3 respectively, and the monitoring fluctuation value and data retention value of the review associated node are 9.6 and 15.5 respectively, then the collection of the review sensor node and the review associated node

[0054] The total number of acquisition linkage nodes is marked as SPK, the acquisition linkage values ​​of the acquisition linkage nodes are summed and averaged to obtain the acquisition linkage mean value, which is marked as TMJ, and the total number of review-related nodes is marked as DXC. The collection effective value AZY of the review sensor node is obtained, where d1 is the total number coefficient of collection linkage nodes, d2 is the collection linkage mean coefficient, and d3 is the total number coefficient of review associated nodes. The value of d1 is 0.58, the value of d2 is 0.42, and the value of d3 is 0.99.

[0055] Set the effective collection threshold. The effective collection threshold is the system setting threshold and can be modified according to actual needs.

[0056] When the collection validity value is greater than or equal to the collection validity threshold, the monitoring data collected by the review sensor node within n time periods before the current system time will be retained to generate a data retention record for the review sensor node. The data retention record includes the data retention time (the time for retaining the monitoring data).

[0057] When the collection effective value is less than the collection effective threshold, the monitoring data collected by the review sensor node within n time periods before the current time of the system will be eliminated.

[0058] The monitoring fluctuation value is obtained in the following way: obtain m data monitoring logs of the sensor node before the current time of the system, sort all data monitoring logs in the order of monitoring time, calculate the difference between two adjacent data after sorting, and obtain the data fluctuation amount, set the expected fluctuation amount, which is the system preset value and can be modified according to actual needs (different types of data correspond to different expected fluctuation amounts), when the data fluctuation amount is greater than or equal to the expected fluctuation amount, increase the number of data fluctuations by one, when the data fluctuation amount is less than the expected fluctuation amount, do not do the corresponding processing, sum up all the data fluctuation times, obtain the total number of data fluctuations, and mark it as PWZ, and use the formula The monitoring fluctuation value FSC of the sensor node is obtained, where b1 is the total number of data fluctuation coefficients, b2 is the number of data monitoring log coefficients, the value of b1 is 0.98, and the value of b2 is 0.91.

[0059] The data retention value is obtained by the following method: obtain all data retention records of the sensor node before the current time of the system, sort all data retention records in the order of data retention time, calculate the time difference between the data retention time of two adjacent data retention records after sorting, and obtain the data retention interval, sum up all data retention intervals and take the average to obtain the average retention interval, and mark it as GHD, mark the total number of data retention records as ERW, and use the formula The data retention value VBX of the sensor node is obtained, where c1 is the average retention interval coefficient, c2 is the total number coefficient of data retention records, the value of c1 is 0.73, and the value of c2 is 0.69.

[0060] A data review module is set up to review and analyze the data collected by the sensor nodes through data association, and it is no longer necessary for the corresponding personnel to arrive at the sensor node location to conduct data review and analysis.

[0061] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0062] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0063] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0064] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0066] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0067] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0068] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data collection and monitoring method based on a wireless sensor network, characterized in that: The steps include: Step 1: Collect monitoring data of each sensor node based on a dynamic period; Step 2: Obtain the abnormal review value of the sensor node in real time, and determine whether the data monitored by the sensor node needs to be reviewed based on the comparison result between the abnormal review value and its threshold; Step 3: Review the data monitored by the sensor nodes to determine whether the data monitored by the sensor nodes needs to be eliminated; The abnormal review value of the sensor node is obtained by the following method: Get all the data monitoring logs of the sensor node within n time periods before the current time of the system, sort all the data monitoring logs in the order of monitoring time, calculate the time difference between the two adjacent monitoring times after sorting, get the monitoring interval, sum up all the monitoring intervals and take the average, get the average monitoring interval, and mark it as TSP, mark the total number of data monitoring logs as REW, and use the formula The abnormal review value TDS of the sensor node is obtained, where a1 is the average monitoring interval coefficient and a2 is the total number coefficient of data monitoring logs; Based on the comparison result between the abnormal review value and its threshold, it is determined whether the data monitored by the sensor node needs to be reviewed, specifically: setting the abnormal review threshold, when the abnormal review value of the sensor node is greater than or equal to the abnormal review threshold, marking the sensor node as a review sensor node, obtaining the monitoring position of the review sensor node, and marking the remaining sensor nodes in the monitoring position as co-located sensor nodes; The data monitored by the sensor node is reviewed, specifically: the monitoring data type of the review sensor node is marked as the review data type, the monitoring data type of the co-location sensor node is marked as the co-location data type, and the co-location sensor node of the co-location data type associated with the review data type is marked as the review associated node through the knowledge graph association analysis; Obtain the monitoring fluctuation value and data retention value of the review sensor node and mark them as [A1, A2]. Obtain the monitoring fluctuation value and data retention value of the review associated node and mark them as [B1, B2]. Use the cosine similarity algorithm to obtain the collection linkage value of the review sensor node and the review associated node, set the collection linkage threshold, and when the collection linkage value is greater than or equal to the collection linkage threshold, mark the review associated node as a collection linkage node. Determine whether the data monitored by the sensor node needs to be eliminated. Specifically, the total number of acquisition linkage nodes is marked as SPK, the acquisition linkage values ​​of the acquisition linkage nodes are summed and averaged to obtain the acquisition linkage mean value, which is marked as TMJ, and the total number of review-related nodes is marked as DXC. Get the collection effective value AZY of the review sensor node, where d1 is the total number coefficient of collection linkage nodes, d2 is the collection linkage mean coefficient, and d3 is the total number coefficient of review association nodes. Set the collection effective threshold. When the collection effective value is greater than or equal to the collection effective threshold, retain the monitoring data collected by the review sensor node within n time periods before the current time of the system, and generate the data retention record of the review sensor node. The data retention record includes the data retention time. When the collection effective value is less than the collection effective threshold, the monitoring data collected by the review sensor node within n time periods before the current time of the system will be eliminated.

2. A data acquisition and monitoring system based on a wireless sensor network, applied to the data acquisition and monitoring method based on a wireless sensor network according to claim 1, characterized in that: It includes collection monitoring module, review and determination module, and data review module; The acquisition monitoring module acquires monitoring data of each sensor node based on a dynamic period; The review determination module is used to obtain the abnormal review value of the sensor node in real time, and determine whether the data monitored by the sensor node needs to be reviewed based on the comparison result between the abnormal review value and its threshold value; The data review module is used to review the data monitored by the sensor node and determine whether the data monitored by the sensor node needs to be discarded.

3. The data acquisition and monitoring system based on wireless sensor network according to claim 2 is characterized in that: The monitoring data of each sensor node is collected based on a dynamic period. Specifically, based on a preset period, at each period node, the monitoring data of each sensor node is obtained, a data monitoring log is generated, the data type of the data monitoring log is obtained, and the expected data range of this type of data is obtained. When the data volume of the data monitoring log is outside the expected data range, the preset period of the sensor node is shortened.

4. The data acquisition and monitoring system based on wireless sensor network according to claim 3 is characterized in that: The data monitoring log includes data type, data volume, monitoring time, and monitoring location.

5. The data acquisition and monitoring system based on wireless sensor network according to claim 2 is characterized in that: The monitoring fluctuation value is obtained by the following method: obtain m data monitoring logs of the sensor node before the current time of the system, sort all data monitoring logs in the order of monitoring time, calculate the difference between two adjacent data after sorting, obtain the data fluctuation amount, set the expected fluctuation amount, when the data fluctuation amount is greater than or equal to the expected fluctuation amount, increase the number of data fluctuations by one, sum up all the data fluctuation times, obtain the total number of data fluctuations, and mark it as PWZ, and use the formula The monitoring fluctuation value FSC of the sensor node is obtained, where b1 is the total number of data fluctuation coefficients and b2 is the number of data monitoring log coefficients.

6. The data acquisition and monitoring system based on wireless sensor network according to claim 2 is characterized in that: The data retention value is obtained by the following method: obtain all data retention records of the sensor node before the current time of the system, sort all data retention records in the order of data retention time, calculate the time difference between the data retention time of two adjacent data retention records after sorting, and obtain the data retention interval, sum up all data retention intervals and take the average to obtain the average retention interval, and mark it as GHD, mark the total number of data retention records as ERW, and use the formula The data retention value VBX of the sensor node is obtained, where c1 is the average retention interval coefficient and c2 is the total number coefficient of data retention records.

Images