Massive internet of things device contribution sampling evaluation method and device

Abstract

The application provides a mass Internet of Things device contribution sampling evaluation method and device. When mass Internet of Things device contribution sampling evaluation is performed, qualification is determined to determine qualified Internet of Things devices, and then device contribution points are calculated according to data of the qualified Internet of Things devices. When qualification is determined, a verifiable random parameter and a hash of real data generated in an evaluation period are introduced as parameters into qualification determination, so that fairness and verifiability of sampling can be ensured. When device contribution points are calculated, reported data of the Internet of Things devices in a statistical window is used as a coefficient to participate in calculation of device contribution weight, so that the contribution points obtained by each Internet of Things device are determined by data generated by the device.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) access technology, and in particular to a method and apparatus for sampling and evaluating the contribution of massive IoT devices. Background Technology

[0002] The Internet of Things (IoT) refers to the use of various devices and technologies, such as information sensors, RFID, GPS, infrared sensors, and laser scanners, to collect real-time information on any object or process that needs to be monitored, connected, or interacted with. This information includes sound, light, heat, electricity, mechanics, chemistry, biology, and location data, and is accessed through various possible networks to achieve ubiquitous connectivity between things and between things and people. This enables intelligent sensing, identification, and management of objects and processes. In other words, the IoT is an internet that connects everything; it's an extension and expansion of the internet, forming a vast network that combines various information sensing devices with the internet, enabling interconnection and interoperability between people, machines, and things anytime, anywhere.

[0003] The value of IoT data lies in its circulation, and a large number of distributed IoT devices exist in people's daily lives. To encourage IoT device owners (i.e., data owners) to more actively connect their devices to the platform and provide valuable, real data, incentives are needed. Therefore, a fair and equitable sampling evaluation method based on the contributions of a massive number of IoT devices is urgently required to incentivize device owners to connect their devices to the IoT platform. Summary of the Invention

[0004] The purpose of this application is to provide a method, verification method, storage device and verification device for sampling evaluation of contributions from a large number of Internet of Things (IoT) devices, thereby overcoming at least to some extent one or more problems caused by the limitations and defects of related technologies.

[0005] To achieve the above and other related objectives, this application provides a method for sampling and evaluating the contributions of a large number of IoT devices, applied to an IoT platform, including:

[0006] Obtain random parameters for the evaluation period, as well as the hash value and number of reported data entries for each first IoT device within the evaluation period;

[0007] The seed value for each first IoT device is determined based on the random parameters, the preset contribution threshold, the hash value of the reported data of each first IoT device, and the number of reported data entries.

[0008] The seed value of each first IoT device is compared with a preset judgment threshold, and the qualified first IoT device is selected as the second IoT device based on the comparison result.

[0009] Obtain the reported data and contribution calculation coefficient of each of the second IoT devices in the statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT devices in the statistical window;

[0010] The contribution weight of each second IoT device is calculated based on the total number of data entries reported by each second IoT device within the statistical window and the contribution calculation coefficient.

[0011] The contribution ratio of each second IoT device is obtained based on the contribution weight of each second IoT device.

[0012] The number of contribution points for each second IoT device is calculated based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period.

[0013] In an optional embodiment of this application, obtaining the random parameters of the evaluation period includes:

[0014] At the beginning of each evaluation period, a pair of keys is randomly generated, including a first private key and a first public key;

[0015] Random parameters for each of the first IoT devices are generated based on the first private key and the device public key of each of the first IoT devices.

[0016] In an optional embodiment of this application, generating random parameters for each of the first IoT devices based on the first private key and the device public key of each first IoT device includes:

[0017] Based on the first private key and the device public key of each of the first IoT devices, a shared key for each of the first IoT devices is generated using a key exchange algorithm or by concatenation.

[0018] In an optional embodiment of this application, obtaining the hash value of the reported data of each first IoT device during the evaluation period includes:

[0019] Obtain the reported data of each of the first IoT devices during the evaluation period from the server;

[0020] Each piece of data reported by each of the first IoT devices is hashed to obtain the hash value of each piece of data reported by each IoT device, which is then used as the hash value of the reported data of each of the first IoT devices.

[0021] In an optional embodiment of this application, obtaining the hash value of the reported data of each first IoT device during the evaluation period includes:

[0022] Obtain the hash value of the block generated within the evaluation period, and use it as the hash value of the reported data of each of the first IoT devices.

[0023] In an optional embodiment of this application, the seed value for each of the first IoT devices is determined based on the random parameters, a preset contribution threshold, the hash value of the reported data from each of the first IoT devices, and the number of reported data entries, including:

[0024] Multiply the random parameter of each first IoT device by the number of reported data entries, and combine it with the hash value of the reported data to form a combined parameter, which serves as the combined parameter for each first IoT device.

[0025] A hash operation is performed on the combined parameters of each of the first IoT devices to obtain a random number for each of the first IoT devices.

[0026] The random number of each first IoT device is moduloed by the preset contribution threshold, and the modulo value is used as the seed value of each first IoT device.

[0027] In one optional embodiment of this application,

[0028] In the step of obtaining the total number of data entries reported by each of the second IoT devices within the statistical window and the contribution calculation coefficient, the contribution calculation coefficient includes the data authenticity coefficient, the normalized contribution amount, and the device type coefficient. The window data volume coefficient is calculated from the contribution amount of the data reported by the second IoT device within the statistical window.

[0029] In an optional embodiment of this application, the contribution weight W of the k-th second IoT device k Obtained using the following formula:

[0030] W k =T k *C k *Z k *M k

[0031] Among them, Z k T represents the total number of data entries reported by the second IoT device within the statistics window. k Let C be the data authenticity coefficient of the k-th second IoT device. k M is the normalized contribution of the k-th second IoT device. k Let be the device type coefficient of the kth second IoT device.

[0032] In one optional embodiment of this application,

[0033] C k =MIN(CE) k ,threshold)+POWER(MAX(0,CE k -threshold),α),

[0034] Among them, CE k The function is used to calculate the total contribution of the k-th second IoT device within the statistical window. Threshold is a preset contribution threshold, α is a constant, and the POWER() function returns the power of the given number.

[0035] In an optional embodiment of this application, obtaining the contribution ratio of each second IoT device based on the contribution weight of each second IoT device includes:

[0036] Sum the contribution weights of all the second IoT devices to obtain the weighted sum;

[0037] Divide the contribution weight of each second IoT device by the sum of the weights to obtain the contribution ratio of each second IoT device.

[0038] To achieve the above and other related objectives, this application provides a massive IoT device contribution sampling evaluation device, applied to an IoT platform, comprising:

[0039] The first data acquisition module is used to acquire random parameters of the evaluation period, as well as the hash value and number of reported data of each first IoT device within the evaluation period.

[0040] The seed value calculation module is used to determine the seed value of each first IoT device based on the random parameters, the preset contribution threshold, the hash value of the reported data of each first IoT device, and the number of reported data entries.

[0041] The qualification determination module is used to compare the seed value of each first IoT device with a preset determination threshold, and select the qualified first IoT devices as second IoT devices based on the comparison result.

[0042] The second data acquisition module is used to acquire the reported data and contribution calculation coefficient of each of the second IoT devices in the statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT devices in the statistical window;

[0043] The contribution weight calculation module is used to calculate the contribution weight of each second IoT device based on the total number of data entries reported by each second IoT device in the statistical window and the contribution calculation coefficient.

[0044] A contribution ratio calculation model is used to obtain the contribution ratio of each second IoT device based on the contribution weight of each second IoT device;

[0045] The contribution point calculation module is used to calculate the contribution points of each second IoT device based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period.

[0046] The massive IoT device contribution sampling evaluation method and storage device of this application, when conducting contribution sampling evaluation, obtains random parameters of the evaluation period, as well as the hash value and number of reported data of each first IoT device within the evaluation period; determines a seed value for each first IoT device based on the random parameters, a preset contribution threshold, and the hash value and number of reported data of each first IoT device; compares the seed value of each first IoT device with a preset judgment threshold, and selects qualified first IoT devices as second IoT devices based on the comparison result; obtains the reported data and contribution calculation coefficient of each second IoT device within a statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT device within the statistical window; calculates the contribution weight of each second IoT device based on the total number of reported data and contribution calculation coefficient of each second IoT device within the statistical window; obtains the contribution ratio of each second IoT device based on the contribution weight of each second IoT device; and calculates the number of contribution points of each second IoT device based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period. When determining eligibility, verifiable random parameters and hashes of real data generated within the evaluation period are introduced as parameters to ensure the fairness, impartiality, and verifiability of the sampling. When calculating the number of device contribution points, the data reported by IoT devices within the statistical window are used as coefficients in the calculation of device contribution weights, so that the number of contribution points obtained by each IoT device is determined by the data generated by the device. Attached Figure Description

[0047] Figure 1 The flowchart of the massive IoT device contribution sampling evaluation method of this application is shown in a specific embodiment.

[0048] Figure 2 A sub-flowchart of step S20 in a specific embodiment is shown.

[0049] Figure 3 The diagram illustrates the functional blocks of the massive IoT device contribution sampling evaluation device of this application in a specific embodiment. Detailed Implementation

[0050] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application.

[0051] To incentivize IoT device owners to actively connect their devices to the platform, this application discloses a massive IoT device contribution sampling evaluation method applicable to IoT platforms. Figure 1 A flowchart of a method for sampling and evaluating the contribution of a large number of IoT devices provided in one embodiment of this application is shown.

[0052] In this application, contribution is reflected by data reported by an IoT device, representing measurable actions performed by the device for a specific purpose within a specific period. In power generation applications, where the IoT device can be a distributed power generation device, such as photovoltaic power generation equipment (e.g., photovoltaic solar panels), wind power generation equipment, tidal power generation equipment, etc., the amount of power generated, the timeliness of the reported data, and the accuracy of the data are all measurable contribution behaviors.

[0053] Of course, in this application, the IoT device can also be an IoT device capable of generating valuable reported data in non-power generation application scenarios, such as an energy storage system. The idle capacity provided by the energy storage system to absorb excess electrical energy (charging) or release energy (discharging) according to external demand can be considered a contribution of the energy storage system.

[0054] like Figure 1 As shown in an exemplary embodiment, the massive IoT device contribution sampling evaluation method includes the following steps:

[0055] S10, obtain the random parameters of the evaluation period, as well as the hash value and the number of reported data of each first IoT device within the evaluation period;

[0056] S20, determine the seed value of each first IoT device based on the random parameters, the preset contribution threshold, the hash value of the reported data of each first IoT device, and the number of reported data entries;

[0057] S30, compare the seed value of each first IoT device with a preset judgment threshold, and select the qualified first IoT device as the second IoT device according to the comparison result;

[0058] S40, obtain the reported data and contribution calculation coefficient of each of the second IoT devices in the statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT devices in the statistical window;

[0059] S50, calculate the contribution weight of each second IoT device based on the total number of data entries reported by each second IoT device in the statistical window and the contribution calculation coefficient;

[0060] S60, obtain the contribution ratio of each second IoT device according to the contribution weight of each second IoT device;

[0061] S70, calculate the number of contribution points for each of the second IoT devices based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period.

[0062] When conducting a massive sampling evaluation of the contributions of IoT devices, a qualification assessment is first performed to identify eligible IoT devices. These eligible devices are then selected from the vast pool of IoT devices as a sample. The contribution points for each eligible IoT device are then calculated based on their data. During the qualification assessment, verifiable random parameters, such as the hash of real data generated within the evaluation period, are incorporated to ensure fairness and impartiality in the sampling process, guaranteeing that the contribution reflected in the sample is consistent with the contribution reflected in the full sample. When calculating the device contribution points, the data reported by the IoT devices within the statistical window is used as a coefficient in the calculation of the device contribution weight, ensuring that the contribution points awarded to each IoT device are determined by the data generated by that device.

[0063] The following is a detailed explanation of each step.

[0064] First, step S10 is executed to obtain the random parameters of the evaluation period, as well as the hash value and the number of reported data of each first IoT device within the evaluation period.

[0065] To incentivize IoT device owners to actively connect their devices to the platform and generate valuable, real-world data, the platform sets a data reporting frequency for IoT devices, such as reporting data to the platform's server every 5 minutes. This results in the server storing a large amount of reported data from IoT devices, allowing for contribution allocation based on this stored data. For example, if an evaluation period is one hour, then approximately 12 data reports might be generated within that period.

[0066] Since the IoT platform connects to a massive number of IoT devices, conducting a full evaluation would be too costly. Therefore, when allocating the number of contributions, it is necessary to first determine the eligibility of IoT devices in order to sample eligible IoT devices (defined as second IoT devices) from the massive number of IoT devices (defined as first IoT devices) during the evaluation period.

[0067] When determining the eligibility of IoT devices, it is necessary to first obtain the random parameters of the evaluation period, as well as the hash value and the number of reported data of each first IoT device within the evaluation period.

[0068] The random parameters for obtaining the evaluation period include: at the beginning of each evaluation period, the IoT platform first randomly generates a key pair, including a first private key SK and a first public key PK. The first public key PK can be published to the network, for example, through smart contract content, while the first private key SK is kept by the IoT platform; then, based on the first private key SK and the device public key PK of each of the first IoT devices... 设备 This generates a shared key for each of the first IoT devices, serving as a random parameter for each of the first IoT devices. This is based on the device public key (PK) of each IoT device. 设备 Since it is unique, the shared key obtained by each IoT device is different, that is, the random parameters of each of the first IoT devices are different.

[0069] Specifically, this can be done based on the first private key SK and the device public key PK of each of the first IoT devices. 设备 The ECDH key exchange algorithm is used to generate a shared key ECDH(SK,PK) for each of the first IoT devices. 设备 ECDH stands for Elliptic Curve Diffie-Hellman Key Exchange, primarily used to establish secure shared encrypted data over an insecure channel. Of course, DH, RSA, and other key exchange algorithms can also be used to exchange data based on the first private key SK and the device public key PK of each of the first IoT devices. 设备 Generate a shared key for each of the first IoT devices.

[0070] In one optional embodiment, when obtaining the random parameters for the evaluation period, the random parameters for each of the first IoT devices can also be generated by concatenating the first private key and the device public key of each first IoT device. Of course, other calculation methods or concatenation methods can also be used to generate the random parameters for each of the first IoT devices based on the first private key and the device public key of each first IoT device, and no limitation is imposed here.

[0071] There are two different ways to obtain the hash value of the reported data of each first IoT device within the evaluation period. The first way is to first obtain the reported data of each first IoT device within the evaluation period from the server; then, perform a hash operation on each piece of reported data of each first IoT device to obtain the hash value Y1,…,Y of each reported data of each IoT device. N , which is the hash value of the reported data of each of the first IoT devices, where N is the number of reported data entries of the first IoT device within the evaluation period.

[0072] The second approach is based on a trusted blockchain environment. IoT devices can upload data to the blockchain, and the hash values ​​of the blocks generated within the evaluation period can be directly obtained as the hash values ​​of the data reported by each of the first IoT devices. For example, assuming the block height of the blockchain is H at the beginning of an evaluation period and H+a at the end, this means that 'a' blocks were generated during the evaluation period, and the hash values ​​(BlockHash) of these 'a' blocks can be obtained. H ,…,BlockHash H+a This serves as the hash value of the data reported by each of the first IoT devices. By introducing blockchain features during the eligibility process, the fairness of the sampling can be increased.

[0073] Next, step S20 is executed to determine the seed value of each first IoT device based on the random parameters of each first IoT device, the hash value of the reported data, the number of reported data entries, and the preset contribution threshold.

[0074] like Figure 2 As shown, in an optional embodiment, the seed value of each first IoT device is determined based on the random parameters of each first IoT device, the hash value of the reported data, the number of reported data entries, and a preset contribution threshold, specifically including:

[0075] Step S21: Multiply the random parameter of each first IoT device by the number of reported data entries, and combine it with the hash value of the reported data to form a combined parameter, which is used as the combined parameter for each first IoT device.

[0076] Step S22: Perform a hash operation on the combined parameters of each of the first IoT devices to obtain a random number for each of the first IoT devices;

[0077] Step S23: Take the modulo of the random number of each first IoT device with the preset contribution threshold, and use the modulo value as the seed value of each first IoT device.

[0078] When the shared key is used as the random parameter, the seed value SEED has the following two expressions depending on how the hash value of the data reported by the first IoT device is obtained:

[0079] For the hash value of the data reported by the first IoT device obtained through the first method, the seed value SEED is calculated using the following formula:

[0080] SEED = SHA256(ECDH(SK,PK device)*N,Y1,…,Y N )%, where Y1,…,Y N , where is the hash value of each reported data from the first IoT device, N is the number of reported data from the first IoT device within the evaluation period, and K is the preset contribution threshold. SHA256 is a cryptographic hash function that produces a 256-bit hash value for any message length, called a message digest, which can be represented by a 64-bit hexadecimal string.

[0081] For the hash value of the data reported by the first IoT device obtained through the second method, the seed value SEED is calculated using the following formula:

[0082] SEED = SHA256(ECDH(SK,PK device)*N,BlockHash) H ,…,BlockHash H+a )%K, where BlockHash H ,…,BlockHash H+a Let N be the hash value of 'a' blocks generated by the blockchain within the evaluation period, N be the number of data entries reported by the first IoT device within the evaluation period, and K be the preset contribution threshold. If one block is generated per minute, then in an evaluation period of 1 hour, 60 blocks can be generated within one evaluation period, and the hash value of these 60 blocks can be used as the hash value of the data reported by the first IoT device.

[0083] By first multiplying the random parameter by the number of reported valid data entries and combining it with the hash value of the reported data to form a combined parameter, and then performing a hash operation on the combined parameter, a sufficiently random random number can be generated. This can increase the fairness of qualification determination and contribution allocation, and ensure that the contribution reflected by the sample is similar to the contribution reflected by the full sample.

[0084] Next, step S30 is executed, in which the seed value of each first IoT device is compared with a preset judgment threshold, and the qualified first IoT device is selected as the second IoT device based on the comparison result.

[0085] Specifically, when the seed value of the first IoT device is equal to the preset judgment threshold, it indicates that the first IoT device is qualified; otherwise, it indicates that the first IoT device is not qualified. As an example, the preset judgment threshold is 0.

[0086] It should be noted that, for fairness, if the first IoT device does not report any data during the evaluation period, i.e., does not generate any data records, it cannot participate in the contribution allocation. In other words, the first IoT device is only eligible if its seed value equals the preset judgment threshold and it reports legitimate data during the evaluation period.

[0087] It should be noted that, since the data of the first IoT device connected to the IoT platform is large, the participation qualification judgment logic can be accelerated by using multi-threading. All qualified first IoT device information (i.e., second IoT device information) is passed to the contribution point calculation step (steps S40-S70) to calculate the contribution points.

[0088] In conducting eligibility assessment, i.e., sampling, this application uses verifiable random parameters and reported data hashes to mathematically ensure that (1) the sampled items have an average distribution; (2) the sampling results are unpredictable before the sampling time, making it impractical to increase the probability of selecting a specific device through prediction; and (3) the sampling results can be verified using verifiable random parameters after the sampling time, ensuring sampling fairness. These three characteristics ensure that the evaluation results of the sampled items are consistent with those of the full sample, i.e., the contribution reflected by the sampled items is similar to the contribution reflected by the full sample.

[0089] Next, steps S40 and S50 are executed to obtain the reported data and contribution calculation coefficient of each second IoT device in the statistics window. The contribution weight of each second IoT device is calculated based on the total number of data entries reported by each second IoT device in the statistics window and the contribution calculation coefficient. At least one of the contribution calculation coefficients is calculated from the reported data of the second IoT device in the statistics window.

[0090] In this application, a statistical window refers to the number of data cycles required for each second IoT device to participate in the contribution calculation. Each statistical window includes multiple cycles, including the evaluation cycle. As an example, if the statistical window is 1 week and the evaluation cycle is 1 hour, then the length of the statistical window is n = 24 * 7 = 168 hours, meaning that one statistical window contains 168 evaluation cycles.

[0091] The system can retrieve the reported data of each second IoT device within each period of the statistical window from the server's database. This data is used to calculate the total number of data entries reported by each second IoT device within the statistical window and at least one contribution calculation coefficient. The contribution calculation coefficient for each second IoT device within the statistical window can be selected based on specific business requirements, thus determining the number of contribution calculation coefficients.

[0092] In one specific embodiment, the contribution calculation coefficient can be selected from a data authenticity coefficient, a normalized contribution amount, and a device type coefficient. The normalized contribution amount is calculated from the total contribution of the second IoT device in the reported data within the statistical window. The data authenticity coefficient T and the device type coefficient M can be predetermined, with M taking different values ​​depending on the device type. The normalized contribution amount C needs to be calculated based on the reported data within the statistical window. For example, T can be 1; M is 1 when the device type is Type A, 2 when the device type is Type B, and 3 when the device type is Type C.

[0093] Assuming a statistical window comprises n periods, the following is to calculate the contribution weight W of the k-th second IoT device. k Includes the following steps:

[0094] according to To calculate the total number of data entries z reported by the k-th second IoT device within the statistics window. k , of which E ki Let be the number of data entries reported by the k-th second IoT device in the i-th period.

[0095] according to To calculate the total contribution CE of the k-th second IoT device within the statistical window. k , of which F ki This represents the contribution of the k-th second IoT device in the i-th period.

[0096] According to C k =MIN(CE) k ,threshold)+POWER(MAX(0,CE k-threshold), α) calculate the normalized contribution C of the k-th second IoT device within the statistical window. k Here, threshold is the preset contribution threshold, α is a constant, and the POWER() function returns the power of the given number. For example, threshold could be 420, and α could be 1 / 5.

[0097] According to W k =T k *C k *Z k *M k Calculate the contribution weight W of the k-th second IoT device within the statistical window. k , among which, T k M is the data authenticity coefficient of the k-th second IoT device. k Let be the device type coefficient of the kth second IoT device.

[0098] When IoT devices are distributed generation devices, the power generation of the distributed generation devices is defined as their contribution. In the reported data, the contribution is the power generation, the preset contribution threshold is the preset power generation threshold, the normalized contribution is the normalized power generation, and the total contribution of devices within the statistical window is the total power generation of distributed generation devices within the statistical window.

[0099] When IoT devices function as energy storage systems, the contribution of the energy storage system is defined as the provision of idle capacity to absorb excess electrical energy (charging) or release energy (discharging) based on external demand. The reported contribution amount is the idle capacity provided by the energy storage system, the preset contribution threshold is the preset idle capacity threshold, the normalized contribution amount is the normalized idle capacity, and the total contribution amount of devices within the statistical window is the total idle capacity provided by the energy storage system within the statistical window.

[0100] Next, step S60 is executed to obtain the contribution ratio of each second IoT device based on the contribution weight of each second IoT device.

[0101] In one specific embodiment, obtaining the contribution ratio of each second IoT device based on the contribution weight of each second IoT device includes:

[0102] Sum the contribution weights of all the second IoT devices to obtain the weight and WS, i.e. Where m represents the total number of eligible IoT devices, or the number of second IoT devices.

[0103] Divide the contribution weight of each second IoT device by the weight and WS to obtain the contribution ratio of each second IoT device, that is, the contribution ratio P of the k-th second IoT device. k It can be represented as P k =W k / WS.

[0104] Finally, step S70 is executed to calculate the contribution points of each second IoT device based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period. After calculating and obtaining the contribution points of each second IoT device, contribution points can be allocated to each second IoT device accordingly. This method can incentivize IoT device owners to connect their devices to the IoT platform.

[0105] The contribution points MT of the kth second IoT device k It can be represented as MT k =A*P k Where A is the total number of contribution points in the evaluation period. A can be globally fixed or dynamically changing in each evaluation period, and is obtained from the configuration table.

[0106] As shown above, this application, when conducting a massive IoT device contribution sampling evaluation, first performs eligibility assessment to identify eligible IoT devices. These eligible devices are then selected from the massive IoT device pool as a sampling sample. Next, the device contribution points are calculated based on the data from these eligible IoT devices. By incorporating verification random parameters—specifically, the hash of the real data generated during the evaluation period—into the eligibility assessment, the fairness, impartiality, and verifiability of the eligibility assessment, i.e., the sampling, are ensured. This guarantees that the contribution reflected in the sampling sample is consistent with the contribution reflected in the full sample, thus ensuring consistency between the evaluation results of the sampling sample and the full sample. Furthermore, by using the data reported by IoT devices within the statistical window as a coefficient in the calculation of device contribution weights when calculating device contribution points, the contribution points obtained by each IoT device are determined by the data generated by the device. Additionally, by introducing blockchain characteristics during the eligibility assessment, the fairness of contribution point allocation is enhanced.

[0107] Figure 3 This is a functional block diagram illustrating the sampling and evaluation device for a large number of IoT devices provided in one embodiment of this application. Please refer to... Figure 3 As shown, the massive IoT device contribution sampling evaluation device 11 includes a first data acquisition module 111, a seed value calculation module 112, a qualification determination module 113, a second data acquisition module 114, a contribution weight calculation module 115, a contribution ratio calculation model 116, and a contribution point calculation module 117.

[0108] The first data acquisition module 111 is used to acquire random parameters of the evaluation period, as well as the hash value and number of reported data of each first IoT device within the evaluation period.

[0109] Seed value calculation module 112 is used to determine the seed value of each first IoT device based on the random parameters, the preset contribution threshold, the hash value of the reported data of each first IoT device, and the number of reported data entries.

[0110] The qualification determination module 113 is used to compare the seed value of each first IoT device with a preset determination threshold, and select the qualified first IoT device as the second IoT device according to the comparison result.

[0111] The second data acquisition module 114 is used to acquire the reported data and contribution calculation coefficient of each of the second IoT devices in the statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT devices in the statistical window;

[0112] The contribution weight calculation module 115 is used to calculate the contribution weight of each second IoT device based on the total number of data entries reported by each second IoT device in the statistical window and the contribution calculation coefficient.

[0113] The contribution ratio calculation model 116 is used to obtain the contribution ratio of each second IoT device based on the contribution weight of each second IoT device;

[0114] The contribution point calculation module 117 is used to calculate the contribution point of each second IoT device based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period.

[0115] It should be noted that the massive IoT device contribution sampling evaluation device 11 in this embodiment is a device corresponding to the massive IoT device contribution sampling evaluation method described above. The functional modules in the massive IoT device contribution sampling evaluation device 11 correspond to the corresponding steps in the massive IoT device contribution sampling evaluation method. The massive IoT device contribution sampling evaluation device 11 in this embodiment can be implemented in conjunction with the massive IoT device contribution sampling evaluation method. Accordingly, the relevant technical details mentioned in the massive IoT device contribution sampling evaluation device 11 in this embodiment can also be applied to the massive IoT device contribution sampling evaluation method described above.

[0116] It should be noted that the functional modules of the aforementioned massive IoT device contribution sampling evaluation device 11 can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can all be implemented via software calls through processing elements. Additionally, these modules can be fully or partially integrated together, or implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. During implementation, some or all steps of the above method, or the functional modules mentioned above, can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0117] In summary, the massive IoT device contribution sampling evaluation method and storage device of this application, when conducting sampling evaluation, obtains random parameters of the evaluation period, as well as the hash value and number of reported data of each first IoT device within the evaluation period; determines a seed value for each first IoT device based on the random parameters, a preset contribution threshold, and the hash value and number of reported data of each first IoT device; compares the seed value of each first IoT device with a preset judgment threshold, and selects qualified first IoT devices as second IoT devices based on the comparison result; obtains the reported data and contribution calculation coefficient of each second IoT device within a statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT device within the statistical window; calculates the contribution weight of each second IoT device based on the total number of reported data and contribution calculation coefficient of each second IoT device within the statistical window; obtains the contribution ratio of each second IoT device based on the contribution weight of each second IoT device; and calculates the number of contribution points of each second IoT device based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period. When determining eligibility, verifiable random parameters, such as the hash of real data generated within the evaluation period, are introduced into the eligibility determination process. This ensures the fairness, impartiality, and verifiability of the eligibility determination, i.e., the sampling, guaranteeing that the contribution reflected by the sample is consistent with the contribution reflected by the full sample, thus ensuring consistency between the evaluation results of the sample and the full sample. When calculating the number of device contribution points, the data reported by IoT devices within the statistical window is used as a coefficient in the calculation of the device contribution weight, so that the number of contribution points obtained by each IoT device is determined by the data generated by the device.

[0118] It should be noted that the illustrations provided in this embodiment are only schematic representations of the basic concept of this application. Therefore, the illustrations only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0119] The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for sampling and evaluating the contributions of a large number of Internet of Things (IoT) devices, characterized in that, Applications in IoT platforms, including: Obtain random parameters for the evaluation period, as well as the hash value and number of reported data entries for each first IoT device within the evaluation period; The seed value for each first IoT device is determined based on the random parameters, the preset contribution threshold, the hash value of the reported data of each first IoT device, and the number of reported data entries. The seed value of each first IoT device is compared with a preset judgment threshold, and the qualified first IoT device is selected as the second IoT device based on the comparison result. Obtain the reported data and contribution calculation coefficient of each of the second IoT devices in the statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT devices in the statistical window; The contribution weight of each second IoT device is calculated based on the total number of data entries reported by each second IoT device within the statistical window and the contribution calculation coefficient. The contribution ratio of each second IoT device is obtained based on the contribution weight of each second IoT device. The number of contribution points for each second IoT device is calculated based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period.

2. The method for sampling and evaluating the contribution of massive IoT devices according to claim 1, characterized in that, Obtain random parameters for the evaluation period, including: At the beginning of each evaluation period, a pair of keys is randomly generated, including a first private key and a first public key; Random parameters for each of the first IoT devices are generated based on the first private key and the device public key of each of the first IoT devices.

3. The method for sampling and evaluating the contribution of massive IoT devices according to claim 2, characterized in that, Based on the first private key and the device public key of each of the first IoT devices, random parameters for each of the first IoT devices are generated, including: Based on the first private key and the device public key of each of the first IoT devices, random parameters for each of the first IoT devices are generated using a key exchange algorithm or by concatenation.

4. The method for sampling and evaluating the contribution of massive IoT devices according to claim 1, characterized in that, Obtaining the hash value of the reported data of each first IoT device within the evaluation period includes: Obtain the reported data of each of the first IoT devices during the evaluation period from the server; Each piece of data reported by each of the first IoT devices is hashed to obtain the hash value of each piece of data reported by each IoT device, which is then used as the hash value of the reported data of each of the first IoT devices.

5. The method for sampling and evaluating the contribution of massive IoT devices according to claim 1, characterized in that, Obtaining the hash value of the reported data of each first IoT device within the evaluation period includes: Obtain the hash value of each block generated within the evaluation period, and use it as the hash value of the reported data of each of the first IoT devices.

6. The method for sampling and evaluating the contribution of massive IoT devices according to claim 1, characterized in that, The seed value for each of the first IoT devices is determined based on the random parameters, the preset contribution threshold, the hash value of the reported data from each first IoT device, and the number of reported data entries, including: Multiply the random parameter of each first IoT device by the number of reported data entries, and combine it with the hash value of the reported data to form a combined parameter, which serves as the combined parameter for each first IoT device. A hash operation is performed on the combined parameters of each of the first IoT devices to obtain a random number for each of the first IoT devices. The random number of each first IoT device is moduloed by the preset contribution threshold, and the modulo value is used as the seed value of each first IoT device.

7. The method for sampling and evaluating the contribution of massive IoT devices according to claim 1, characterized in that, In the step of obtaining the total number of data entries reported by each of the second IoT devices within the statistical window and the contribution calculation coefficient, the contribution calculation coefficient includes a data authenticity coefficient, a normalized contribution amount, and a device type coefficient, wherein the normalized contribution amount is calculated from the contribution amount of the data reported by the second IoT device within the statistical window.

8. The method for sampling and evaluating the contribution of massive IoT devices according to claim 7, characterized in that, The contribution weight W of the kth second IoT device k Obtained using the following formula: W k =T k *C k *Z k *M k Among them, Z k T represents the total number of data entries reported by the second IoT device within the statistics window. k Let C be the data authenticity coefficient of the k-th second IoT device. k M is the normalized contribution of the k-th second IoT device. k Let be the device type coefficient of the kth second IoT device.

9. The method for sampling and evaluating the contribution of massive IoT devices according to claim 8, characterized in that, C k =MIN(CE k ,threshold)+POWER(MAX(0,CE k -threshold),α) Among them, CE k The function is used to calculate the total contribution of the k-th second IoT device within the statistical window. Threshold is a preset contribution threshold, α is a constant, and the POWER() function returns the power of the given number.

10. The method for sampling and evaluating the contribution of massive IoT devices according to claim 1, characterized in that, The contribution ratio of each second IoT device is obtained based on its contribution weight, including: Sum the contribution weights of all the second IoT devices to obtain the weighted sum; Divide the contribution weight of each second IoT device by the sum of the weights to obtain the contribution ratio of each second IoT device.

11. A sampling and evaluation device for the contribution of massive Internet of Things (IoT) devices, characterized in that, Applications in IoT platforms, including: The first data acquisition module is used to acquire random parameters of the evaluation period, as well as the hash value and number of reported data of each first IoT device within the evaluation period. The seed value calculation module is used to determine the seed value of each first IoT device based on the random parameters, the preset contribution threshold, the hash value of the reported data of each first IoT device, and the number of reported data entries. The qualification determination module is used to compare the seed value of each first IoT device with a preset determination threshold, and select the qualified first IoT devices as second IoT devices based on the comparison result. The second data acquisition module is used to acquire the reported data and contribution calculation coefficient of each of the second IoT devices in the statistical window, wherein at least one of the contribution calculation coefficients is calculated from the reported data of the second IoT devices in the statistical window; The contribution weight calculation module is used to calculate the contribution weight of each second IoT device based on the total number of data entries reported by each second IoT device in the statistical window and the contribution calculation coefficient. A contribution ratio calculation model is used to obtain the contribution ratio of each second IoT device based on the contribution weight of each second IoT device; The contribution point calculation module is used to calculate the contribution points of each second IoT device based on the contribution ratio of each second IoT device and the total number of contribution points in the evaluation period.

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