Access control method and system suitable for sensing equipment of Internet of Things

Abstract

The invention relates to the technical field of Internet of Things access control, in particular to an access control method and system suitable for Internet of Things sensing devices, and the method comprises the steps: obtaining related data for each sensing device in the Internet of Things; analyzing a data change difference degree between the to-be-uploaded data sequence of the sensing equipment and each historical uploaded data sequence, and constructing a historical difference index; analyzing the degree of correlation between each sensing device and other sensing devices in the cluster where the sensing device is located with respect to the data sequence to be uploaded, and constructing a key cooperation coefficient of each sensing device in combination with the historical difference index of each sensing device and the deviation condition of the historical difference index in the cluster where each sensing device is located; constructing a priority index of each sensing device according to the total uploading frequency of each sensing device and the prominent condition of the size of the uploaded data packet; and in combination with the priority index of each sensing device, performing access control on the sensing device of the Internet of Things. The access effect of the sensing device can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things access control, and in particular to an access control method and system applicable to Internet of Things sensing devices. Background Art

[0002] The access of IoT sensing devices is the basis for data collection and processing, intelligent control and remote management; however, since the IoT sensing devices have a large amount of data and diverse types, the sensing devices may cause channel congestion when they are accessed, resulting in higher access delays and affecting data transmission efficiency.

[0003] In order to improve the access effect of IoT for sensing devices, the prior art proposes an ACB control mechanism, which uses the ACB factor to control the access of devices. However, in the existing ACB control technology, a random number of 0-1 is generated for the device that needs to access, and devices that generate random numbers less than or equal to the ACB factor are allowed to access, thereby achieving access control and alleviating channel congestion. However, ACB control is a random access control technology that does not take into account the importance or urgency of the sensing device data, so it may cause delayed access to important data, thereby affecting the access effect. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an access control method and system suitable for IoT sensing devices. The technical solutions adopted are as follows:

[0005] The present application embodiment provides an access control method applicable to an IoT sensing device, comprising the following steps:

[0006] For each sensing device in the Internet of Things, the transmission data of the sensing device at each collection time is obtained to form a data sequence to be uploaded by the sensing device, the total number of data uploads and the size of the uploaded data packets of the sensing device in the historical period are counted, and each historical upload data sequence of the sensing device is obtained;

[0007] Analyze the data change difference between the data sequence to be uploaded by the sensing device and its historical uploaded data sequence, and construct a historical difference index of the sensing device;

[0008] Cluster the historical difference indexes of all sensing devices, analyze the correlation between each sensing device and other sensing devices in its cluster regarding the sequence of data to be uploaded, and construct the key synergy coefficient of each sensing device by combining the historical difference index of each sensing device and the deviation of the historical difference index within the cluster where each sensing device is located;

[0009] Construct a priority index for each sensing device based on the total number of uploads of each sensing device in the historical period and the prominence of the size of uploaded data packets among all sensing devices;

[0010] Based on the priority index of each sensing device, access control is performed on the IoT sensing devices.

[0011] Preferably, the calculation method of the historical difference index of the sensing device is:

[0012] In the formula, A i is the historical difference index of the i-th sensing device; n is the total number of data uploads of the i-th sensing device in the historical period; B i，a Take the absolute value of the difference between the peak-to-valley difference index of the data sequence to be uploaded and the ath historical uploaded data sequence of the i-th sensing device, and obtain the peak-to-valley difference index of the data sequence to be uploaded and the historical uploaded data sequence according to the data changes of the peak-to-valley values ​​in the data sequence to be uploaded and the historical uploaded data sequence respectively; K i，a P is the absolute value of the difference between the change rate of the data sequence to be uploaded by the i-th sensing device and the a-th historical uploaded data sequence; i，a is the similarity index between the ith sensing device and the ath historical uploaded data sequence; τ is a constant to avoid the denominator being zero.

[0013] Preferably, obtaining the peak-to-valley difference index of the to-be-uploaded data sequence and the historical uploaded data sequence further includes:

[0014] For the data sequence to be uploaded, extract the peak values ​​and valley values ​​in the data sequence to be uploaded, calculate the average value of the absolute difference between each peak value and its preceding and succeeding data, record it as the mutation index of each peak value, calculate the product of the variance and the mean value of the mutation index of all peak values, and record it as the mutation difference index of the peak value, accordingly, adopt the calculation method of the mutation difference index of the peak value to obtain the mutation difference index of the valley value, and take the average value of the mutation difference index of the peak value and the mutation difference index of the valley value as the peak-valley difference index of the data sequence to be uploaded;

[0015] For each historical uploaded data sequence, the peak-valley difference index of the historical uploaded data sequence is calculated using the method for obtaining the peak-valley difference index of the data sequence to be uploaded.

[0016] Preferably, the acquisition of the change rate further includes:

[0017] For the data sequence to be uploaded and each historical uploaded data sequence of the sensing device, first-order difference sequences are obtained respectively, and the average of the absolute values ​​of all elements in the first-order difference sequence is used as the change rate of the data sequence to be uploaded and each historical uploaded data sequence respectively.

[0018] Preferably, the key synergy coefficient of each sensing device is calculated as follows:

[0019] In the formula, C i is the key synergy coefficient of the i-th sensing device; A i is the historical difference index of the i-th sensing device; is the intra-cluster synergy index of the i-th sensing device, which is calculated by the correlation between the i-th sensing device and other sensing devices in its cluster regarding the sequence of data to be uploaded; G is the ratio of the number of historical difference indexes in the cluster where the i-th sensing device is located to the number of all historical difference indexes; D i is the mean value of the historical difference index in the cluster where the i-th sensing device is located; D max It is the maximum value of the mean of historical difference index within all clusters.

[0020] Preferably, the method for calculating the intra-cluster synergy index of the i-th sensing device further comprises:

[0021] The mutual information value of the data sequence to be uploaded between the i-th sensing device and other sensing devices in its cluster is calculated, and the mean of all mutual information values ​​is recorded as the intra-cluster synergy index of the i-th sensing device.

[0022] Preferably, the calculation method of the priority index of each sensing device is:

[0023] In the formula, F i is the priority index of the i-th sensing device; C i is the key synergy coefficient of the i-th sensing device; S ci is the upload significance index of the total number of uploads of the i-th sensing device; S bi is the upload significance index of the upload data packet size of the i-th sensing device.

[0024] Preferably, the method for calculating the upload significance index includes:

[0025] For the total number of uploads of the sensing device in the historical period, the average value of the absolute difference between the total number of uploads of the sensing device and the total number of uploads of all other sensing devices is calculated, and recorded as the uploading outlier index of the sensing device. The ratio of the maximum value between the total number of uploads of the sensing device and the total number of all uploads is recorded as the uploading most proportion of the sensing device. The product of the uploading outlier index and the uploading most proportion is taken as the uploading significance index of the total number of uploads of the sensing device.

[0026] According to the upload data packet size of the perception device in the historical period, the upload significance index of the upload data packet size of the perception device is obtained by adopting the calculation method of the upload significance index of the total number of uploads.

[0027] Preferably, the access control of the IoT sensing device further includes:

[0028] The inverse of the priority index of each sensing device in the Internet of Things is used as the delay coefficient of each sensing device, and the delay coefficient of each sensing device is normalized. The normalized delay coefficient replaces the random number between 0 and 1 generated for the sensing device in the ACB mechanism, and an access request is sent to the sensing device whose normalized delay coefficient is less than or equal to the ACB factor in the ACB mechanism.

[0029] An embodiment of the present application also provides an access control system applicable to Internet of Things sensing devices, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned access control methods applicable to Internet of Things sensing devices.

[0030] From the above, it can be seen that the access control method and system applicable to IoT sensing devices provided by the present application have at least the following beneficial effects:

[0031] This application constructs a historical difference index to calculate whether there is a large difference between the latest data collected by the perception device and the historical uploaded data, evaluates the abnormality of the latest data, and preliminarily determines its priority; then constructs a key synergy coefficient to calculate the synergy change between the latest collected data of the perception device, so as to further evaluate the importance of the collected data; then constructs a priority index to comprehensively evaluate the access priority of the perception device by comparing the upload differences of different perception devices;

[0032] The existing ACB access control technology only generates random numbers for the sensing devices that need to upload, and the access of important data is delayed because the priority of the sensing devices is not considered; this application constructs a priority index to comprehensively evaluate the access priority of various sensing devices, and uses it to replace the generated random numbers, thereby achieving access control for the sensing devices of the Internet of Things while ensuring that important data is accessed first, thereby improving the access effect of the sensing devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1A flowchart of the steps of the access control method applicable to IoT sensing devices provided in this application. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the access control method and system for IoT sensing devices proposed in the present application, its specific implementation method, structure, features and effects are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0036] Unless otherwise specified and limited, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application.

[0037] The following is a detailed description of the specific scheme of the access control method and system applicable to IoT sensing devices provided by the present application with reference to the accompanying drawings.

[0038] See also Figure 1 , which shows a flowchart of a method for access control of an IoT sensing device provided by an embodiment of the present application, including the following steps:

[0039] Step 1: For each sensing device in the Internet of Things, obtain the transmission data of the sensing device at each collection time to form the data sequence to be uploaded by the sensing device, count the total number of data uploads and the size of the uploaded data packets of the sensing device in the historical period, and obtain the historical upload data sequences of the sensing device.

[0040] Since there will be multiple sensing devices in a local IoT system, and if the sensing devices collect relevant data and transmit it to the IoT system in real time, a large number of devices will be connected and data will be transmitted at the same time, which will cause channel congestion. Therefore, in the IoT system, the sensing devices usually collect certain data and then connect to the IoT system to transmit the data.

[0041] In this embodiment, the i-th sensing device is taken as an example for analysis and explanation. After the transmission data of the i-th sensing device is collected, when it needs to be connected to the Internet of Things system again for data transmission, the data to be uploaded this time is arranged in the chronological order of data collection to construct the data sequence to be uploaded for the i-th sensing device.

[0042] Furthermore, this embodiment performs statistical analysis on the data transmission status of each sensing device in the historical time period. Specifically, the total number of data uploads, the size of the uploaded data packet, and the historical data of each upload of the i-th sensing device within the historical time period, which in this embodiment is the 5 hours before the current data collection, are obtained from the database of the Internet of Things system; wherein each uploaded historical data is also arranged in the order of data collection time to construct each historical upload data sequence.

[0043] At this point, the relevant data of the i-th sensing device is obtained, and the above process of this embodiment is used to obtain the relevant data of all sensing devices in the same Internet of Things. In order to eliminate the dimensional influence between the data, all the data are subjected to Z-score normalization processing. The specific normalization processing process is an existing well-known technology and will not be repeated in this embodiment.

[0044] Step 2: Analyze the data change difference between the data sequence to be uploaded by the sensing device and its historical uploaded data sequences, and construct a historical difference index.

[0045] In the IoT system, after a sensing device collects a certain amount of data, it will establish a connection with the IoT system for data transmission. If at the same time point, the i-th sensing device is transmitting data, and other sensing devices also try to initiate data transmission requests, it will cause competition for network channel resources. When multiple sensing devices compete for limited bandwidth at the same time, it will cause channel congestion, which will affect the efficiency of data transmission and delay the access and data transmission of subsequent sensing devices, which may cause urgent data to be delayed. Therefore, it is necessary to calculate the upload priority of all sensing devices to ensure that critical data is transmitted first.

[0046] If under normal circumstances, all data collected by sensing devices are normal, then no matter what kind of sensing device, the latest collected data has a certain correlation with the uploaded historical data, and the size of the uploaded data packets is relatively consistent. However, if some abnormal situation occurs, such as a fire caused by an abnormal environment, which causes the temperature to rise, the smoke concentration to rise, and the humidity to drop, it will cause a large difference between the data collected by some sensing devices and the previously uploaded data. Therefore, it is necessary to upload the data with large differences to the Internet of Things system in a timely manner to detect whether an abnormal situation has really occurred.

[0047] Furthermore, this embodiment analyzes the differences between the collected data and the historical data, and analyzes the differences between the data sequence to be uploaded of the i-th sensing device and the historical uploaded data sequence that has been uploaded, so as to preliminarily evaluate whether the data collected by the i-th sensing device has undergone significant changes, and whether it needs to be promptly connected to the Internet of Things system for data transmission.

[0048] Specifically, taking the ath historical upload data sequence uploaded by the i-th sensing device to the Internet of Things system as an example, the difference between the data sequence to be uploaded and the historical upload data sequence a is calculated.

[0049] All peaks and valleys in the data sequence to be uploaded are obtained through the peak and valley detection algorithm, and the average of the absolute difference between each peak and its previous and subsequent data is calculated, which is recorded as the mutation index of each peak. The product of the variance and mean of the mutation index of all peaks is calculated and recorded as the mutation difference index of the peak. Accordingly, the calculation method of the mutation difference index of the peak is adopted to obtain the mutation difference index of the valley, and the average of the mutation difference index of the peak and the mutation difference index of the valley is used as the peak-valley difference index of the data sequence to be uploaded. The larger the peak-valley difference index is, the greater and more drastic the difference in the change between the data is reflected in the data sequence to be uploaded.

[0050] Correspondingly, for each historical uploaded data sequence, the peak-valley difference index of the data sequence to be uploaded is obtained by using the peak-valley difference index acquisition method to calculate the peak-valley difference index of the historical uploaded data sequence.

[0051] Further, the first-order difference sequence of the data sequence to be uploaded is obtained, and the mean of the absolute values ​​of all elements in the first-order difference sequence is calculated, which is recorded as the rate of change of the data sequence to be uploaded. In the same way, the rate of change of the historical uploaded data sequence a is obtained. Further, according to the deviation of the peak-to-valley difference index between the data sequence to be uploaded of each sensing device and each historical uploaded data sequence and the deviation of the change rate, the historical difference index of each sensing device is constructed. In this embodiment, the specific calculation formula is:

[0052] In the formula, A i is the historical difference index of the i-th sensing device; n is the total number of data uploads of the i-th sensing device; B i，a K is the absolute value of the difference between the peak-to-valley difference index of the data sequence to be uploaded by the i-th sensing device and the a-th historical uploaded data sequence; i，a is the absolute value of the difference between the change rate of the data sequence to be uploaded by the i-th sensing device and the a-th historical uploaded data sequence; P i，ais the similarity index between the ith sensing device and the ath historical uploaded data sequence, expressed as the Pearson correlation coefficient between the data sequence to be uploaded and the ath historical uploaded data sequence; τ is a constant to avoid the denominator being 0, ranging from 0 to 0.1, and is 0.01 in this implementation.

[0053] Among them, B i，a It reflects the difference in data fluctuation between the current data to be uploaded and the historical data uploaded for the ath time. If B i，a The larger the K is, the greater the fluctuation characteristics of the current data have changed compared with the historical data. i，a It reflects the difference in the degree of data change between the data to be uploaded and the historical data uploaded for the ath time. If K i，a The larger the value is, the greater the difference in the degree of data change between the current data and the historical data uploaded for the ath time.

[0054] In the above formula, the similarity index P i，a reflects the overall correlation between the data to be uploaded and the historical data uploaded for the ath time. If P i，a The smaller it is, the more inconsistent the data trend is between the current data and the historical data. It should be noted that: considering that the calculation result of the Pearson similarity coefficient may be a negative value, 1 is added to the denominator to ensure that the feature significance reflected by the similarity index will not be changed, and the denominator can be guaranteed to be non-negative, which will not affect the overall calculation.

[0055] Therefore, if the historical difference index is larger, it can reflect that the latest data collected by the i-th sensing device is different from the previous data, and the possibility of changes in the substances monitored by the i-th sensing device is greater. At this time, it is necessary to upload the data collected by the i-th sensing device to the Internet of Things system in time to detect whether an abnormal situation has occurred. Therefore, if the historical difference index is larger, the access priority of the i-th sensing device will be higher when accessing the Internet of Things.

[0056] Step 3: Cluster the historical difference indexes of all sensing devices, analyze the correlation between each sensing device and other sensing devices in its cluster regarding the data sequence to be uploaded, and construct the key synergy coefficient of each sensing device by combining the historical difference index of each sensing device and the deviation of the historical difference index within the cluster where each sensing device is located.

[0057] For IoT sensing devices in the same monitoring environment, when the monitoring environment changes abnormally, the data collected by multiple sensing devices will change at the same time. That is, in the event of an abnormality, the data collected by multiple sensing devices will change synergistically. If the ith sensing device has strong synergistic changes with multiple sensing devices, it means that the possibility of changes in the environment monitored by the ith sensing device is greater, and it is more necessary to upload data in a timely manner.

[0058] The historical difference index of all sensing devices is calculated using the calculation method of the historical difference index of the i-th sensing device mentioned above; and the historical difference index of all sensing devices is clustered as the input of the k-means clustering algorithm, and the number of clusters is obtained by the elbow rule. The specific clustering process is an existing well-known technology and will not be described in detail in this embodiment.

[0059] In this embodiment, the mutual information value of the data sequence to be uploaded between the i-th sensing device and the other sensing devices in the cluster where the i-th sensing device is located is calculated by the mutual information method (MI), and the mean of all mutual information values ​​is recorded as the intra-cluster synergy index of the i-th sensing device. The larger the intra-cluster synergy index, the stronger the correlation dependence between the data sequence to be uploaded of the i-th sensing device and the other sensing devices in the cluster where the i-th sensing device is located, and the greater the synergy change trend.

[0060] Therefore, in this embodiment, according to the intra-cluster synergy index corresponding to each sensing device, combined with the historical difference index of each sensing device and the deviation of the historical difference index within the cluster where each sensing device is located, the key synergy coefficient of each sensing device is calculated. In this embodiment, the calculation formula is:

[0061] In the formula, C i is the key synergy coefficient of the i-th sensing device; A i is the historical difference index of the i-th sensing device; is the intra-cluster synergy index of the i-th sensing device; G is the ratio of the number of historical difference indexes in the cluster where the i-th sensing device is located to the number of all historical difference indexes; D i is the mean value of the historical difference index in the cluster where the i-th sensing device is located; D max It is the maximum value of the mean of historical difference index within all clusters.

[0062] If the historical difference index A i The larger the value is, the greater the difference between the latest data collected by the i-th sensing device and the historical data is. The larger the value is and the larger the proportion of the number G is, the stronger the dependence between the latest collected data of all the sensing devices in the cluster where the i-th sensing device is located is, and the greater the proportion of the number of elements in the cluster where the i-th sensing device is located is, that is, the more devices have a strong collaborative relationship with the i-th sensing device. max -D i The smaller the value is, the closer the average historical difference of the cluster where the i-th sensing device is located is to the maximum value among all the clusters where the i-th sensing devices are located, and the greater the possibility that the latest data collected by the sensing devices in the cluster where the i-th sensing device is located will be significantly different from the historical data.

[0063] Therefore, if the key synergy coefficient C i The larger it is, the greater the difference between the latest collected data of the i-th perception device and the historical data, and the more devices there are with strong coordinated changes in the latest collected data and the i-th perception device; the more important the latest data collected by the i-th perception device is, and the more it needs to be transmitted in a timely manner.

[0064] Step 4: Construct a priority index for each sensing device based on the total number of uploads of each sensing device in the historical period and the prominence of the size of uploaded data packets among all sensing devices.

[0065] Furthermore, in this embodiment, the access priority of the i-th sensing device is analyzed by calculating the data upload difference between the i-th sensing device and other sensing devices. Within a fixed time range, if the uploading frequency of the sensing device is higher and the number of uploading times is more, it usually means that the material or environmental parameter monitored by the sensing device is more important. Therefore, the importance of the sensing device can be further measured by analyzing the upload frequency.

[0066] Therefore, in this embodiment, the average of the absolute differences between the total number of uploads of the i-th sensing device and the total number of uploads of all other sensing devices is calculated, and the average is recorded as the upload outlier index. The ratio of the total number of uploads of the i-th sensing device to the maximum value of all total uploads is recorded as the maximum upload ratio of the i-th sensing device.

[0067] The product of the upload outlier index and the highest upload percentage is recorded as the upload significance index of the total number of uploads of the i-th sensing device. The larger the upload significance index, the greater the possibility that the monitoring data collected by the i-th sensing device is more important than other sensing devices, and the higher the upload priority is needed.

[0068] Furthermore, in this embodiment, the size of the uploaded data packet of the i-th perception device is used to reflect its upload priority; the larger the data packet that the i-th perception device needs to upload, the longer it takes to upload the data, the greater the impact on other perception devices that need to upload data, and the lower the data access priority.

[0069] Similarly, in this embodiment, for the upload data packet size of each perception device in the historical period, the upload significance index of the total upload times is calculated using the upload significance index calculation method and process to obtain the upload data packet size of the i-th perception device.

[0070] According to the upload significance index of the total number of uploads of each sensing device and the upload significance index of the size of the uploaded data packet, combined with the key coordination coefficient of each sensing device, the priority index of each sensing device is calculated. In this embodiment, the specific calculation formula is:

[0071] In the formula, F i is the priority index of the i-th sensing device; C i is the key synergy coefficient of the i-th sensing device; S ci is the upload significance index of the total number of uploads of the i-th sensing device; S bi is the upload significance index of the upload data packet size of the i-th sensing device.

[0072] If the key synergy coefficient C i The larger the value, the more important the latest data collected by the i-th sensing device is, and the more necessary it is to upload the data in time for further detection; if the upload significance index S of the total number of uploads is ci The larger the value is, the higher the upload frequency of the i-th sensing device is compared with other sensing devices, which reflects the higher importance of the monitoring data collected by the i-th sensing device. bi The smaller it is, the smaller the uploaded data packet of the i-th sensing device is compared with other sensing devices, and the less system resources are occupied.

[0073] Therefore, if the priority index F i The larger the value is, the higher the relative importance and urgency of the i-th sensing device in the IoT system. The data collected by the i-th sensing device not only has significant changes compared with historical data, but also has a higher upload frequency, and the data is more important. At the same time, the uploaded data packets are relatively small, occupying less system resources. Therefore, if the priority index F i The larger the value is, the higher the priority of the i-th sensing device is when accessing.

[0074] Step 5: Based on the priority index of each sensing device, access control is performed on the IoT sensing devices.

[0075] According to the above process of this embodiment, the priority index calculation method and process of the i-th sensing device are adopted to obtain the priority index of all sensing devices in the same Internet of Things system, and the reciprocal of the priority index is used as the delay coefficient of the sensing device.

[0076] The delay coefficients of various sensing devices are sigmoid normalized, and the normalized delay coefficients are used to replace the random numbers between 0 and 1 generated for the sensing devices in the traditional ACB mechanism, and the normalized delay coefficients of each sensing device are compared with the ACB factor of the ACB mechanism. Access requests are sent to sensing devices whose normalized delay coefficients are less than or equal to the ACB factor, thereby optimizing the ACB control technology, which can not only achieve access control of IoT sensing devices and avoid network congestion, but also ensure that important data can be accessed to the IoT system in a timely manner. It should be noted that the ACB mechanism in this embodiment is the ACB access control technology, and the specific process is the existing technology, which will not be described in detail in this embodiment.

[0077] Based on the same inventive concept as the above method, an embodiment of the present application also provides an access control system applicable to Internet of Things sensing devices, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the access control methods applicable to Internet of Things sensing devices are implemented.

[0078] It is to be understood that the sequence of the embodiments of the present application described above is for description only and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0080] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the protection scope of the present application.

Claims

1. An access control method applicable to IoT sensing devices, characterized in that: The following steps are involved: For each sensing device in the Internet of Things, the transmission data of the sensing device at each collection time is obtained to form a data sequence to be uploaded by the sensing device, the total number of data uploads and the size of the uploaded data packets of the sensing device in the historical period are counted, and each historical upload data sequence of the sensing device is obtained; Analyze the data change difference between the data sequence to be uploaded by the sensing device and its historical uploaded data sequence, and construct a historical difference index of the sensing device; Cluster the historical difference indexes of all sensing devices, analyze the correlation between each sensing device and other sensing devices in its cluster regarding the sequence of data to be uploaded, and construct the key synergy coefficient of each sensing device by combining the historical difference index of each sensing device and the deviation of the historical difference index within the cluster where each sensing device is located; Construct a priority index for each sensing device based on the total number of uploads of each sensing device in the historical period and the prominence of the size of uploaded data packets among all sensing devices; Based on the priority index of each sensing device, access control is performed on the IoT sensing devices.

2. The access control method applicable to the IoT sensing device according to claim 1, characterized in that: The calculation method of the historical difference index of the sensing device is: In the formula, A i is the historical difference index of the i-th sensing device; n is the total number of data uploads of the i-th sensing device in the historical period; B i，a Take the absolute value of the difference between the peak-to-valley difference index of the data sequence to be uploaded and the ath historical uploaded data sequence of the i-th sensing device, and obtain the peak-to-valley difference index of the data sequence to be uploaded and the historical uploaded data sequence according to the data changes of the peak-to-valley values ​​in the data sequence to be uploaded and the historical uploaded data sequence respectively; K i，a P is the absolute value of the difference between the change rate of the data sequence to be uploaded by the i-th sensing device and the a-th historical uploaded data sequence; i， is the similarity index between the ith sensing device and the ath historical uploaded data sequence; τ is a constant to avoid the denominator being zero.

3. The access control method applicable to the IoT sensing device according to claim 2, characterized in that: The acquisition of the peak-to-valley difference index of the to-be-uploaded data sequence and the historical uploaded data sequence further includes: For the data sequence to be uploaded, extract the peak values ​​and valley values ​​in the data sequence to be uploaded, calculate the average value of the absolute difference between each peak value and its preceding and succeeding data, record it as the mutation index of each peak value, calculate the product of the variance and the mean value of the mutation index of all peak values, and record it as the mutation difference index of the peak value, accordingly, adopt the calculation method of the mutation difference index of the peak value to obtain the mutation difference index of the valley value, and take the average value of the mutation difference index of the peak value and the mutation difference index of the valley value as the peak-valley difference index of the data sequence to be uploaded; For each historical uploaded data sequence, the peak-valley difference index of the historical uploaded data sequence is calculated using the method for obtaining the peak-valley difference index of the data sequence to be uploaded.

4. The access control method applicable to IoT sensing devices according to claim 1, characterized in that: The acquisition of the change rate further includes: For the data sequence to be uploaded and each historical uploaded data sequence of the sensing device, first-order difference sequences are obtained respectively, and the average of the absolute values ​​of all elements in the first-order difference sequence is used as the change rate of the data sequence to be uploaded and each historical uploaded data sequence respectively.

5. The access control method applicable to IoT sensing devices according to claim 1, characterized in that: The corresponding calculation method of the key synergy coefficient of each sensing device is: In the formula, C i is the key synergy coefficient of the i-th sensing device; A i is the historical difference index of the i-th sensing device; is the intra-cluster synergy index of the i-th sensing device, which is calculated by the correlation between the i-th sensing device and other sensing devices in its cluster regarding the sequence of data to be uploaded; G is the ratio of the number of historical difference indexes in the cluster where the i-th sensing device is located to the number of all historical difference indexes; D i is the mean value of the historical difference index in the cluster where the i-th sensing device is located; D max It is the maximum value of the mean of historical difference index within all clusters.

6. The access control method applicable to IoT sensing devices according to claim 5, characterized in that: The method for calculating the intra-cluster synergy index of the i-th sensing device further includes: The mutual information value of the data sequence to be uploaded between the i-th sensing device and other sensing devices in its cluster is calculated, and the mean of all mutual information values ​​is recorded as the intra-cluster synergy index of the i-th sensing device.

7. The access control method applicable to IoT sensing devices according to claim 1, characterized in that: The calculation method of the priority index of each sensing device is: In the formula, F i is the priority index of the i-th sensing device; C i is the key synergy coefficient of the i-th sensing device; S ci is the upload significance index of the total number of uploads of the i-th sensing device; S bi is the upload significance index of the upload data packet size of the i-th sensing device.

8. The access control method applicable to IoT sensing devices according to claim 7, characterized in that: The calculation method of the upload significance index includes: For the total number of uploads of the sensing device in the historical period, the average value of the absolute difference between the total number of uploads of the sensing device and the total number of uploads of all other sensing devices is calculated, and recorded as the uploading outlier index of the sensing device. The ratio of the maximum value between the total number of uploads of the sensing device and the total number of all uploads is recorded as the uploading most proportion of the sensing device. The product of the uploading outlier index and the uploading most proportion is taken as the uploading significance index of the total number of uploads of the sensing device. According to the upload data packet size of the perception device in the historical period, the upload significance index of the upload data packet size of the perception device is obtained by adopting the calculation method of the upload significance index of the total number of uploads.

9. The access control method applicable to IoT sensing devices according to claim 1, characterized in that: The access control of the IoT sensing device further includes: The inverse of the priority index of each sensing device in the Internet of Things is used as the delay coefficient of each sensing device, and the delay coefficient of each sensing device is normalized. The normalized delay coefficient replaces the random number between 0 and 1 generated for the sensing device in the ACB mechanism, and an access request is sent to the sensing device whose normalized delay coefficient is less than or equal to the ACB factor in the ACB mechanism.

10. An access control system for an Internet of Things sensing device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the access control method applicable to the Internet of Things sensing device as described in any one of claims 1 to 9 are implemented.

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