A method for verifying IoT data

Abstract

The present invention relates to the technical field of data transmission, and specifically to an IoT data verification method, comprising: obtaining target IoT data from an IoT device at a preset frequency, configuring an identifier for the target IoT data, and forming tag data. The integrity of the target IoT data in the tag data is verified, and if the verification passes, it is recorded as qualified data; in the order in which the qualified data is generated, the qualified data is copied and sent to a data center, and the original qualified data is saved in a local encrypted database; in the order in which the tag data is generated, the identifiers of the tag data are arranged to generate a data sequence table, and the identifiers of the qualified data are marked in the data sequence table; the first data qualification rate is calculated, and the first data qualification rate is used as a basis for evaluating the data stability of the IoT device. It can realize the verification and transmission of IoT data, ensure the security of the original data of IoT data, and provide traceability conditions for the IoT device system.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to an IoT data verification method. Background Art

[0002] With the rapid development and popularization of Internet of Things technology, the number of IoT devices in the IoT device system is getting larger and larger, and the data generated by IoT devices in the system is getting more and more. In order to ensure the normal use of IoT data, these data need to be verified and processed.

[0003] In the current IoT system, if errors occur during the transmission of IoT data, the relevant data is susceptible to irreversible damage, resulting in data loss. As the amount of IoT data increases, the negative impact of this problem will become more serious.

[0004] In view of this, this application is hereby filed. Summary of the invention

[0005] The purpose of the present invention is to provide an IoT data verification method, which can realize the verification and transmission of IoT data, ensure the security of the original data of IoT data, provide traceability conditions for the IoT device system, and help ensure the continued normal operation of the IoT device system.

[0006] The embodiment of the present invention is achieved as follows:

[0007] An IoT data verification method comprises the following steps:

[0008] S1. Configure a local encrypted database for IoT devices.

[0009] S2. Obtain target IoT data from IoT devices at a preset frequency, configure an identifier for the target IoT data, and form tag data. Verify the integrity of the target IoT data in the tag data, and if the verification passes, record it as qualified data.

[0010] S3. Copy the qualified data in the order in which the qualified data is generated and send it to the data center, and save the original qualified data to the local encrypted database.

[0011] S4. Arrange the identifiers of the tagged data in the order in which the tagged data are generated, generate a data sequence table, and mark the identifiers of the qualified data in the data sequence table.

[0012] S5. Calculate the first data pass rate, and use the first data pass rate as a basis for evaluating the data stability of the IoT device, wherein the first data pass rate = (the number of identifiers marked in the data sequence table) / (the total number of identifiers in the data sequence table).

[0013] Furthermore, the IoT data verification method also includes the following steps:

[0014] S6. After receiving the qualified data, the data center generates matching data. The matching data is composed of an identifier and a timestamp of the corresponding qualified data. The timestamp is used to save the time point when the corresponding qualified data is received.

[0015] S7. Send the matching data to the corresponding IoT device, arrange the matching data in the order of the time points recorded by the timestamps, and arrange the identifiers in the matching data in the order of arrangement to obtain a verification sequence table.

[0016] S8. If the arrangement order of the verification sequence list matches the arrangement order of the identifiers marked in the data sequence list, the transmission of qualified data is deemed to have a low hijacking risk, otherwise it is deemed to have a high hijacking risk.

[0017] Furthermore, the IoT data verification method also includes the following steps:

[0018] S9. In the verification sequence table, the time interval between the time points of the timestamp records of two adjacent matching data is defined as the receiving interval. In the data sequence table, two identifiers corresponding to the receiving interval are determined, and the sending interval of the two qualified data corresponding to the two identifiers is determined according to the arrangement interval of the two identifiers. If the receiving interval matches the sending interval, the transmission of the qualified data is determined to have a low delay risk, otherwise it is determined to have a high delay risk.

[0019] Furthermore, the IoT data verification method also includes the following steps:

[0020] S10, calculating the second data qualification rate, and using the second data qualification rate as a basis for evaluating the transmission success rate when sending data to the data center, wherein the second data qualification rate = (the number of matching data in the verification sequence table) / (the number of identifiers marked in the data sequence table).

[0021] Furthermore, the IoT data verification method also includes the following steps:

[0022] S11. In the data sequence table, a position point is configured for each identifier, and all the position points are arranged in the order of the identifiers in the data sequence table, and the position points are evenly spaced in a line to obtain an arrangement dot matrix, and the spacing between adjacent position points in the arrangement dot matrix is ​​defined as a unit length.

[0023] S12. In the arrangement of the dot matrix, the position points corresponding to the marked identifiers are defined as non-noise points, and the position points corresponding to the unmarked identifiers are defined as noise points. A distance threshold is set, and two adjacent noise points whose distance is less than or equal to the distance threshold are included in the same noise set, and each noise set uses the noise point as an endpoint.

[0024] S13. Taking the arrangement order of the data sequence list as the positive direction, in each noise set, in the positive direction, take the endpoint located at the upstream position as the starting point, and determine the distance value between each non-noise point and the starting point, and classify the distance values ​​of all non-noise points in the noise set into the same data set, and take the total length of the noise set corresponding to the data set as the impact scale of the data set.

[0025] S14. Use the impact scale as the basis for evaluating the error scale of IoT devices.

[0026] S15. Calculate the standard deviation and mean of the data set and use them as the basis for evaluating the fault concentration of IoT devices.

[0027] The beneficial effects of the technical solution of the embodiment of the present invention include:

[0028] In actual work, the IoT data verification method provided by the embodiment of the present invention can set the first pass rate threshold according to actual needs, and the calculation of the first data pass rate can also be performed locally. When the first data pass rate is less than the first pass rate threshold, it means that errors often occur when obtaining target IoT data from the local IoT device. At this time, the local processor can be used to directly send a maintenance request to the data center to maintain the local IoT device in a timely manner.

[0029] The original data that passes the integrity check is stored in a local encrypted database. During the operation of the IoT device system, the local encrypted database can be accessed locally using a key as needed to obtain the original data records of the IoT device, ensuring the security and traceability of the data source, and providing a reference for the maintenance and troubleshooting of the IoT device system.

[0030] In general, the IoT data verification method provided by the embodiment of the present invention can realize the verification and transmission of IoT data, and ensure the security of the original data of IoT data, provide traceability conditions for the IoT device system, and help to ensure the continued normal operation of the IoT device system. In the IoT device system with an increasingly large amount of data, while realizing the verification and transmission of IoT data, it effectively reduces the risk of data being irreversibly damaged, and provides a guarantee for the security of the original data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A schematic diagram of a flow chart of an IoT data verification method provided by an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the dot array arrangement. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] As shown in the specification and claims of this application, unless the context clearly provides an example, the words "a", "an", "the" and the like do not refer to the singular, but may also include the plural. Generally speaking, the term "comprising" only indicates that the steps and elements that have been clearly identified are included, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0037] The flowcharts used in this specification are used to illustrate the operations performed by the system according to the embodiments of this specification. It is understood that the operations of each step are not necessarily performed precisely in order. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0038] Please refer to Figure 1 , this embodiment provides an IoT data verification method, the method comprising the following steps:

[0039] S1. Configure a local encrypted database for IoT devices.

[0040] S2. Obtain target IoT data from the IoT device at a preset frequency, configure an identifier for the target IoT data, and form tagged data. Verify the integrity of the target IoT data in the tagged data. If the verification passes, it is recorded as qualified data. The target IoT data can be the operating parameters of the IoT device, or the detection data of the IoT device (such as a sensor), and is not limited to this. It should be noted that the verification operation of verifying the integrity of the target IoT data in S2 is performed locally. For ease of implementation, a local processor can be equipped for the local IoT device. Optionally, for IoT devices located in the same building, or IoT devices within a certain range, a set of processors can be shared, or local processors can be configured separately for specific IoT devices according to actual needs, and are not limited to this.

[0041] S3. Copy the qualified data in the order in which the qualified data is generated and send it to the data center, and save the original qualified data to the local encrypted database.

[0042] S4. Arrange the identifiers of the tagged data in the order in which the tagged data are generated, generate a data sequence table, and mark the identifiers of the qualified data in the data sequence table.

[0043] S5. Calculate the first data pass rate, and use the first data pass rate as a basis for evaluating the data stability of the IoT device, wherein the first data pass rate = (the number of identifiers marked in the data sequence table) / (the total number of identifiers in the data sequence table).

[0044] In actual work, the first pass rate threshold can be set according to actual needs, and the calculation of the first data pass rate can also be performed locally. When the first data pass rate is less than the first pass rate threshold, it means that errors often occur when obtaining target IoT data from local IoT devices. At this time, the local processor can be used to directly send a maintenance request to the data center to maintain the local IoT device in a timely manner.

[0045] The original data that passes the integrity check is stored in a local encrypted database. During the operation of the IoT device system, the local encrypted database can be accessed locally using a key as needed to obtain the original data records of the IoT device, ensuring the security and traceability of the data source, and providing a reference for the maintenance and troubleshooting of the IoT device system.

[0046] In general, the IoT data verification method provided in this embodiment can realize the verification and transmission of IoT data, and ensure the security of the original data of IoT data, provide traceability conditions for the IoT device system, and help to ensure the continued normal operation of the IoT device system. In the IoT device system with an increasingly large amount of data, while realizing the verification and transmission of IoT data, it effectively reduces the risk of data being irreversibly damaged, and provides a guarantee for the security of the original data.

[0047] It should be noted that the data center can be a server for uniformly collecting and applying IoT data, or a device for aggregating IoT data, and is not limited to this. The actual implementation form of the data center can be flexibly adjusted and set according to actual needs.

[0048] In addition, in S2, when the integrity of the target IoT data in the tag data is verified, if the verification fails, it is recorded as unqualified data. Unqualified data can also be saved separately, thereby ensuring the integrity of the original data of the IoT device. In this way, when maintaining and troubleshooting IoT devices, it can provide a reference for staff and facilitate the troubleshooting of equipment failures.

[0049] In this embodiment, the IoT data verification method further includes the following steps:

[0050] S6. After receiving the qualified data, the data center generates matching data, which is composed of the identifier of the corresponding qualified data and a timestamp, and the timestamp is used to save the time point when the corresponding qualified data is received. In other words, the timestamp saves the time point when the data center receives the corresponding qualified data.

[0051] S7. Send the matching data to the corresponding IoT device, arrange the matching data in the order of the time points recorded by the timestamps, and arrange the identifiers in the matching data in the order of arrangement to obtain a verification sequence table.

[0052] S8. If the arrangement order of the verification sequence list matches the arrangement order of the identifiers marked in the data sequence list, the transmission of qualified data is deemed to have a low hijacking risk, otherwise it is deemed to have a high hijacking risk.

[0053] Optionally, the IoT data verification method further includes the following steps: S9, in the verification sequence table, the time interval between the time points of the timestamp records of two adjacent matching data is defined as the receiving interval. In the data sequence table, two identifiers corresponding to the receiving interval are determined, and the sending interval of the two qualified data corresponding to the two identifiers is determined according to the arrangement interval of the two identifiers.

[0054] For example, if the time interval (receiving interval) between the time points of the timestamp records of two adjacent matching data in the verification sequence table is 0.6S, and in the data sequence table, there are three other identifiers (identifiers corresponding to the marked data whose integrity has not been verified) between the two corresponding identifiers, then the normal time interval range for the data center to receive these two qualified data can be predicted based on the number of other identifiers in the interval.

[0055] If the receiving interval matches the sending interval, that is, 0.3S matches the calculated normal time interval range, the transmission of qualified data is determined to have a low delay risk, otherwise it is determined to have a high delay risk.

[0056] Alternatively, the IoT data verification method further includes the following steps: S10, calculating the second data qualification rate, and using the second data qualification rate as a basis for evaluating the transmission success rate when sending data to the data center. The second data qualification rate = (the number of matching data in the verification sequence table) / (the number of identifiers marked in the data sequence table).

[0057] For the second data pass rate, a second pass rate threshold can also be set according to actual needs. When the second data pass rate is less than the second pass rate threshold, it means that the probability of data loss is high in the process of sending qualified data to the data center. At this time, the local IoT device needs to be maintained. During the maintenance process, the maintenance personnel can use the key to access the local encrypted database locally, so as to supplement the qualified data that was not successfully transmitted before.

[0058] Furthermore, the IoT data verification method also includes the following steps:

[0059] S11. In the data sequence table, a position point is configured for each identifier, and all the position points are arranged in the order of the identifiers in the data sequence table, and the position points are arranged evenly in a line to obtain an arrangement dot matrix, and the spacing between adjacent position points in the arrangement dot matrix is ​​defined as a unit length. For example, if the data sequence table has 14 identifiers, please refer to Figure 2 , then there are 14 position points in the corresponding arrangement lattice.

[0060] S12. In the array of dots, the position points corresponding to the marked identifiers are defined as non-noise points (e.g. Figure 2 The hollow points shown in ), and the position points corresponding to the unmarked identifiers are defined as noise points (such as Figure 2 ). Set a distance threshold (for example, set it to 2 unit lengths), and include two adjacent noise points whose distance is less than or equal to the distance threshold into the same noise set. Each noise set uses the noise point as the endpoint. Then, we can get noise set A and noise set B, as shown in Figure 2 shown.

[0061] S13, with the arrangement order of the data sequence table as the positive direction, in each noise set, in the positive direction, take the endpoint located at the upstream position as the starting point (such as Figure 2 The top position points of noise set A and noise set B in the noise set are determined, and the distance value between each non-noise point and the starting point is determined. The distance values ​​of all non-noise points in the noise set are classified into the same data set, and the total length of the noise set corresponding to the data set is used as the impact scale of the data set. Based on this, for noise set A, the distance value corresponding to the first non-noise point is 2, and the distance value corresponding to the second non-noise point is 4. Its data set is [2, 4], and its impact scale is 5 units in length. Similarly, for noise set B, the distance value corresponding to the first non-noise point is 1, and the distance value corresponding to the second non-noise point is 3. Its data set is [1, 3], and its impact scale is 4 units in length.

[0062] S14. Use the impact scale as the basis for evaluating the error scale of IoT devices. Generally speaking, the larger the impact scale value is, the greater the impact range on the data is, which means that the scale of the data error (error scale) of the IoT device is larger.

[0063] S15. Calculate the standard deviation and mean of the data set and use them as the basis for evaluating the fault concentration of IoT devices.

[0064] In practical work, the size of the actual noise set data set is usually larger than Figure 2 The scale shown in Figure 2 This is a demonstration example only provided to facilitate understanding of the technical solution of this application).

[0065] By counting the standard deviation and mean of the data set based on non-noise points, the degree of dispersion of non-noise points can be determined. The degree of dispersion of noise points can be used to reversely determine the degree of dispersion of noise points, thereby determining the concentration of faults in IoT devices. For example, if the position point corresponding to the average value is located in the middle of the noise set, and the standard deviation reflects that the non-noise points are relatively concentrated, it can be reversely inferred that the noise points are distributed at both ends of the noise set, which means that the appearance of noise points is not completely concentrated, but there is differentiation. The specific situation is not limited to this.

[0066] In summary, the IoT data verification method provided by the embodiment of the present invention can realize the verification and transmission of IoT data, and ensure the security of the original data of IoT data, provide traceability conditions for the IoT device system, and help to ensure the continued normal operation of the IoT device system. In the IoT device system with an increasingly large amount of data, while realizing the verification and transmission of IoT data, it effectively reduces the risk of data being irreversibly damaged, and provides a guarantee for the security of the original data.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An IoT data verification method, characterized in that: The steps include: S1. Configure a local encrypted database for IoT devices. S2. Obtain target IoT data from the IoT device at a preset frequency, configure an identifier for the target IoT data, and form tag data; verify the integrity of the target IoT data in the tag data, and if the verification passes, record it as qualified data; S3, according to the generation order of the qualified data, copy the qualified data and send it to the data center, and save the original qualified data to the local encrypted database; S4, arranging the identifiers of the marked data in the order in which the marked data are generated to generate a data sequence table, and marking the identifiers of the qualified data in the data sequence table; S5. Calculate a first data pass rate, and use the first data pass rate as a basis for evaluating the data stability of the IoT device; wherein the first data pass rate=(the number of the identifiers marked in the data sequence table) / (the total number of the identifiers in the data sequence table); S11, in the data sequence table, assign a position point to each of the identifiers, arrange all the position points in the order of the identifiers in the data sequence table, and arrange the position points evenly spaced in a line to obtain an arrangement dot matrix, and define the spacing between adjacent position points in the arrangement dot matrix as a unit length; S12, in the arrangement dot matrix, defining the position point corresponding to the marked identifier as a non-noise point, and defining the position point corresponding to the unmarked identifier as a noise point; setting a distance threshold, and including two adjacent noise points whose distance is less than or equal to the distance threshold into the same noise set, and each noise set has the noise point as an endpoint; S13, taking the arrangement order of the data sequence table as the positive direction, in each of the noise sets, according to the positive direction, taking the endpoint located at the upstream position as the starting point, and determining the spacing value between each of the non-noise points and the starting point, classifying the spacing values ​​of all the non-noise points in the noise set into the same data set, and taking the total length of the noise set corresponding to the data set as the impact scale of the data set; S14, using the impact scale as a basis for evaluating the error scale of the IoT device; S15. Calculate the standard deviation and mean value of the data set and use them as a basis for evaluating the fault concentration of the IoT device.

2. The IoT data verification method according to claim 1, characterized in that: The IoT data verification method also includes the following steps: S6. After receiving the qualified data, the data center generates matching data, wherein the matching data is composed of the identifier and a timestamp of the corresponding qualified data, and the timestamp is used to store the time point when the corresponding qualified data is received; S7, sending the matching data to the corresponding IoT device, arranging the matching data in the order of the time points recorded by the timestamps, and arranging the identifiers in the matching data in the arrangement order to obtain a verification sequence table; S8. If the arrangement order of the verification sequence table matches the arrangement order of the identifiers indicated in the data sequence table, the transmission of the qualified data is determined to have a low hijacking risk, otherwise it is determined to have a high hijacking risk.

3. The IoT data verification method according to claim 2, characterized in that: The IoT data verification method also includes the following steps: S9. In the verification sequence table, the time interval between the time points of the timestamp records of two adjacent matching data is defined as a receiving interval; in the data sequence table, the two identifiers corresponding to the receiving interval are determined, and the sending interval of the two qualified data corresponding to the two identifiers is determined according to the arrangement interval of the two identifiers; if the receiving interval matches the sending interval, the transmission of the qualified data is determined to have a low delay risk, otherwise it is determined to have a high delay risk.

4. The IoT data verification method according to claim 2, characterized in that: The IoT data verification method also includes the following steps: S10. Calculate the second data qualification rate, and use the second data qualification rate as a basis for evaluating the transmission success rate when sending data to the data center; wherein the second data qualification rate = (the number of matching data in the verification sequence table) / (the number of identifiers marked in the data sequence table).

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