Internet of Things trust analysis method and related device for photovoltaic power station group operation monitoring

Abstract

The present application discloses an Internet of Things trust analysis method and related devices for photovoltaic power station group operation monitoring. Due to the information sufficiency and lack formed by data transmission bandwidth, data transmission rate, and transmission delay, the present application uses the information sufficiency and lack to calculate the Internet of Things trust for photovoltaic power station group operation monitoring, and evaluates the influence of trust on photovoltaic power station group operation, control, and management optimization. This fuzzy probability calculation method of Internet of Things trust for photovoltaic power station group operation monitoring simultaneously reflects the influence of perception system or equipment sufficiency and lack, data transmission bandwidth, data transmission rate, transmission accuracy, and transmission delay, provides theoretical guidance for Internet of Things trust evaluation for photovoltaic power station group operation monitoring, and provides necessary technical support for photovoltaic power station group operation, control, and management optimization based on Internet of Things.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to an Internet of Things trust analysis method and related devices for monitoring the operation of a photovoltaic power station group. Background Art

[0002] The three major application scenarios of 5G are a further deepening of the three major application scenarios of data transmission solutions in the power Internet of Things. Relevant experts and scholars at home and abroad have also divided the power Internet of Things business according to the three major application scenarios of 5G and carried out relevant research work.

[0003] The eMBB scenario mainly meets the needs of some high-bandwidth services, which is an enhancement of data collection application scenarios and business information transmission scenarios. At present, the application of the power Internet of Things in this regard is mainly the big video of the power grid, including substation robot inspection, transmission line drone online monitoring, distribution room video monitoring, mobile on-site construction operation control and emergency on-site self-organizing network comprehensive application. Many researchers have tried to apply 5G technology to certain scenarios and have achieved certain results.

[0004] In terms of trust calculation, there are many research results, such as: calculating the reputation value of nodes in the global scope through multiple iterations of the trust chain, introducing probability theory to solve the trust problem, and giving a trust model algorithm performance based on Bayesian network. The algorithm's time decay and penalty factor are considered when calculating trust, which improves the accuracy of the algorithm and its ability to resist strategic attacks. Trust is dynamically established and developed, and its development trend has certain rules. At the same time, there is a certain correlation between two adjacent transactions. The existing algorithms do not well reflect the time correlation, context correlation and development trend of trust. In order to better describe these characteristics of trust, on the basis of the existing Markov chain model, the steady-state probability is introduced to describe the development trend of trust. After each transaction is completed, the transfer matrix is ​​dynamically updated to more accurately describe the context correlation of trust. At the same time, the time decay factor and penalty incentive factor are added to improve the algorithm's ability to resist attacks. However, there is still little research on the existing technology of fuzzy probability calculation of Internet of Things trust for photovoltaic power station group operation monitoring. Therefore, there is an urgent need for a fuzzy probability calculation method for Internet of Things trust for photovoltaic power station group operation monitoring to calculate the trust of the power Internet of Things and evaluate the impact of trust on the operation, control and management optimization of photovoltaic power station groups. Summary of the invention

[0005] The present application provides an Internet of Things trust analysis method and related devices for photovoltaic power station group operation monitoring, which are used to calculate the trust of the power Internet of Things and evaluate the impact of the trust on the operation, control and management optimization of the photovoltaic power station group.

[0006] In view of this, the first aspect of the present application provides an Internet of Things trust analysis method for photovoltaic power station group operation monitoring, the method comprising:

[0007] According to the characteristic relationship between the output power of photovoltaic power generation system and sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise, the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of photovoltaic power generation system and sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise is calculated by statistical analysis method.

[0008] At the perception layer, the first information benefit value formed by the power Internet of Things providing data collection in the operation monitoring of photovoltaic power station groups is calculated; at the network layer, the second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power station groups is calculated; at the network layer, the third information benefit value formed by the power Internet of Things providing data storage and sharing in the operation monitoring of photovoltaic power station groups is calculated;

[0009] Based on the three-dimensional trapezoidal fuzzy set, the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated;

[0010] Calculate an average value of the trust of the power Internet of Things for all users in the power Internet of Things for photovoltaic power station group operation monitoring according to the first information benefit value, the second information benefit value, the third information benefit value, and the information loss value;

[0011] The trust level of the power Internet of Things is evaluated based on the average value of the trust of all users of the power Internet of Things.

[0012] Optionally, according to the output power and sunshine intensity of the photovoltaic power generation system, a three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error and sunshine intensity of the photovoltaic power generation system is calculated by a statistical analysis method, specifically including:

[0013] In the perception layer of the power Internet of Things, the fuzzy and uncertain sunshine intensity is collected through the perception system;

[0014] Obtain the number of hours during the day at the user's location from the IoT monitoring data center PV The data information of sunshine intensity in each time period is used to calculate the user time period t by using the statistical analysis method according to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine intensity, t=1,2,...,N PV , three-dimensional trapezoidal fuzzy set of fuzzy uncertainty relationship between photovoltaic power generation system output power error and sunshine intensity

[0015]

[0016] In the formula, and are the three-dimensional trapezoidal fuzzy sets of the photovoltaic power generation system output power error related to the sunshine intensity in time period t or the fuzzy numbers and membership coefficients of the photovoltaic power generation system output power error corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to the sunshine intensity in time period t.

[0017] Optionally, the calculating, at the perception layer, of a first information benefit value formed by data collection provided by the power Internet of Things in the operation monitoring of a photovoltaic power station group specifically includes:

[0018] Based on the calculation formula of the first information benefit value, the first information benefit value R formed by the power Internet of Things providing data collection in the operation monitoring of photovoltaic power stations is calculated. PV1 ;

[0019] Wherein, the first information benefit value calculation formula is:

[0020] R PV1 =E[k Mi (M S +M T +M Y +M A +M ΔT )];

[0021] In the formula, R PV1 The information benefit value generated by the data collection provided by the power Internet of Things to users in the operation monitoring of photovoltaic power station groups; k Mi M S The information benefit value generated by the Internet of Things providing sunshine intensity data collection for photovoltaic power stations at the perception layer, k Mi M T The information benefit value generated by the Internet of Things providing data collection for the sunshine time of photovoltaic power stations at the perception layer, k Mi M Y The information benefit value generated by the Internet of Things providing data collection for the sunshine and shadow of the photovoltaic power station group at the perception layer, k Mi M A The information benefit value formed by providing data collection for the sunshine deflection angle for photovoltaic power stations in the perception layer of the Internet of Things, k Mi M ΔT The information benefit value generated by the Internet of Things providing solar temperature rise data collection for photovoltaic power stations at the perception layer, k Mi is the unit information benefit value brought to users by the Internet of Things providing data collection for photovoltaic power stations at the perception layer; E[] is the expected value of [].

[0022] Optionally, the calculating, at the network layer, of the second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of the photovoltaic power station group specifically includes:

[0023] Based on the second information benefit value calculation formula, the second information benefit value R generated by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power station groups is calculated. PV2 ;

[0024] Wherein, the second information benefit value calculation formula is:

[0025] R PV2 =E[k Dvi v Dvi ];

[0026] In the formula, R PV2 The information benefit value generated by the power Internet of Things providing data transmission to users in the operation monitoring of photovoltaic power station groups; k Dvi v Dvi In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission rate of the power Internet of Things.

[0027] Optionally, the third information benefit value formed by providing data storage and sharing in photovoltaic power station group operation monitoring is calculated at the network layer, specifically including:

[0028] Based on the third information benefit value calculation formula, the third information benefit value R formed by the power Internet of Things providing data storage and sharing in the operation monitoring of photovoltaic power stations is calculated. PV3 ;

[0029] Wherein, the third information benefit value calculation formula is:

[0030] R PV3 =E[k DSi S Di ];

[0031] In the formula, R PV3 The information benefit value generated by the power Internet of Things providing data storage and sharing to users in the operation monitoring of photovoltaic power station groups; k DSi S Di In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi It is the unit profit value brought to users by the data storage sharing at the i-th rate provided by the power Internet of Things.

[0032] Optionally, the calculating of the information loss value of the power Internet of Things in the operation monitoring of a photovoltaic power station group based on the three-dimensional trapezoidal fuzzy set specifically includes:

[0033] Substituting the three-dimensional trapezoidal fuzzy set into the information loss value calculation formula, the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated;

[0034] Wherein, the information loss value calculation formula is:

[0035]

[0036] Where LPV is the information loss value caused by the error in data collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine intensity collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in collecting sunshine intensity of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in sunshine time collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in sunshine time collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine shadow collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in the sunshine shadow collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine deflection angle collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in collecting the sunshine deflection angle of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the collection of the rising sunshine temperature in the sensing system of the power Internet of Things. It is the unit information loss value caused by the error in collecting the sunshine temperature rise of the sensing system in the power Internet of Things. They are three-dimensional trapezoidal fuzzy sets of the fuzzy uncertainty relationship between the output power error of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise.

[0037] Optionally, the calculating, according to the first information benefit value, the second information benefit value, the third information benefit value and the information loss value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for monitoring the operation of the photovoltaic power station group specifically includes:

[0038] Substituting the first information benefit value, the second information benefit value, the third information benefit value and the information loss value into the average value calculation formula, and calculating the average value of the trust of the power Internet of Things for all users in the power Internet of Things for monitoring the operation of the photovoltaic power station group;

[0039] The average value calculation formula is:

[0040]

[0041] In the formula, B I is the average trust of all users in the power Internet of Things, R PV1 , R PV2 , R PV3 are the first information benefit value, the second information benefit value, and the third information benefit value, respectively, PV is the information loss value.

[0042] A second aspect of the present application provides an Internet of Things trust analysis system for photovoltaic power station group operation monitoring, the system comprising:

[0043] The first calculation unit is used to calculate the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection angle and sunshine temperature rise respectively by using a statistical analysis method according to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection angle and sunshine temperature rise;

[0044] The second calculation unit is used to calculate, at the perception layer, a first information benefit value formed by the power Internet of Things providing data collection in the operation monitoring of the photovoltaic power station group; at the network layer, calculate the second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of the photovoltaic power station group; at the network layer, calculate the third information benefit value formed by the power Internet of Things providing data storage and sharing in the operation monitoring of the photovoltaic power station group;

[0045] A third calculation unit is used to calculate the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group based on the three-dimensional trapezoidal fuzzy set;

[0046] a fourth calculation unit, configured to calculate an average value of the trust of the power Internet of Things for all users in the power Internet of Things for monitoring the operation of the photovoltaic power station group according to the first information benefit value, the second information benefit value, the third information benefit value, and the information loss value;

[0047] The evaluation unit is used to evaluate the trust level of the power Internet of Things according to the average value of the trust of all users of the power Internet of Things.

[0048] A third aspect of the present application provides an IoT trust analysis device for monitoring the operation of a photovoltaic power station group, wherein the device includes a processor and a memory:

[0049] The memory is used to store program code and transmit the program code to the processor;

[0050] The processor is used to execute the Internet of Things trust analysis method for photovoltaic power station group operation monitoring described in the first aspect according to the instructions in the program code.

[0051] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the Internet of Things trust analysis method for photovoltaic power station group operation monitoring described in the first aspect above.

[0052] It can be seen from the above technical solutions that this application has the following advantages:

[0053] Through the fuzzy probability calculation method of the Internet of Things trust for photovoltaic power station group operation monitoring proposed in this application, the Internet of Things trust for photovoltaic power station group operation monitoring can be calculated. This fuzzy probability calculation method of the Internet of Things trust for photovoltaic power station group operation monitoring reflects the influence of the sufficiency and lack of perception systems or equipment, data transmission bandwidth, data transmission rate, transmission accuracy, and transmission delay at the same time, providing theoretical guidance for the Internet of Things trust evaluation for photovoltaic power station group operation monitoring, and providing necessary technical support for the operation, control and management optimization of photovoltaic power station groups based on the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic flow chart of an Internet of Things trust analysis method for photovoltaic power station group operation monitoring provided in an embodiment of the present application;

[0055] Figure 2 This is a structural schematic diagram of an Internet of Things trust analysis system for monitoring the operation of a photovoltaic power station group provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] See also Figure 1, an Internet of Things trust analysis method for photovoltaic power station group operation monitoring provided in an embodiment of the present application includes:

[0058] Step 101, according to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise, respectively, a statistical analysis method is used to calculate the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise;

[0059] It should be noted that the following is a description of the calculation method of each three-dimensional trapezoidal fuzzy set:

[0060] 1011. In the perception layer of the power Internet of Things, the perception system is used to collect the fuzzy and uncertain sunshine intensity. The N PV The relevant data information of sunshine intensity in each period is calculated and determined by statistical analysis method according to the characteristic relationship between the output power of photovoltaic power generation system and sunshine intensity. PV 3D trapezoidal fuzzy set for the fuzzy uncertainty relationship between the output power error of photovoltaic power generation system and the sunshine intensity

[0061]

[0062] In the formula, and are the three-dimensional trapezoidal fuzzy sets of the photovoltaic power generation system output power error related to the sunshine intensity in time period t or the fuzzy numbers and membership coefficients of the photovoltaic power generation system output power error corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to the sunshine intensity in time period t.

[0063] 1012. In the perception layer of the power Internet of Things, the perception system is used to collect the fuzzy and uncertain sunshine time. The N hours of daytime at the user's location are obtained from the Internet of Things monitoring data center. PV According to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine time, the user time period t (t = 1, 2, ..., N PV 3D trapezoidal fuzzy set for the relationship between the output power error of photovoltaic power generation system and the fuzzy uncertainty of sunshine time

[0064]

[0065] In the formula, and are the three-dimensional trapezoidal fuzzy sets of the output power error of the photovoltaic power generation system related to the sunshine time in time period t, or the fuzzy numbers and membership coefficients of the output power error of the photovoltaic power generation system corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to the time period t and the sunshine time.

[0066] 1013. In the perception layer of the power Internet of Things, the perception system is used to collect the fuzzy and uncertain sunlight shadows. The N PV The relevant data information of sunshine and shadow in each period is calculated and determined by statistical analysis method according to the characteristic relationship between the output power of photovoltaic power generation system and sunshine and shadow. PV 3D trapezoidal fuzzy set of the relationship between the output power error of photovoltaic power generation system and the fuzzy uncertainty of sunshine and shadow

[0067]

[0068] In the formula, and are the three-dimensional trapezoidal fuzzy sets of the photovoltaic power generation system output power error related to the sunshine shadow in time period t, or the fuzzy numbers and membership coefficients of the photovoltaic power generation system output power error corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to sunlight shadow in time period t.

[0069] 1014. In the perception layer of the power Internet of Things, the perception system is used to collect the fuzzy and uncertain sunshine deflection angle. The N PV According to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine deflection angle, the user time period t (t = 1, 2, ..., N PV 3D trapezoidal fuzzy set of the relationship between the output power error of photovoltaic power generation system and the fuzzy uncertainty of sunshine deflection angle

[0070]

[0071] In the formula, and are the three-dimensional trapezoidal fuzzy sets of the photovoltaic power generation system output power error related to the sunshine deflection angle in time period t, or the fuzzy numbers and membership coefficients of the photovoltaic power generation system output power error corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to the sunshine deflection angle in time period t.

[0072] 1015. In the perception layer of the power Internet of Things, the perception system is used to collect the fuzzy and uncertain sunshine temperature rise. The data of the user's location during the daytime N is obtained from the Internet of Things monitoring data center. PV According to the characteristic relationship between the output power of the photovoltaic power generation system and the rise in sunlight temperature, the statistical analysis method is used to calculate and determine the user time period t (t = 1, 2, ..., N PV 3D trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of photovoltaic power generation system and the rise of sunshine temperature

[0073]

[0074] In the formula, and are the three-dimensional trapezoidal fuzzy sets of the photovoltaic power generation system output power error related to the sunshine temperature rise in time period t, or the fuzzy numbers and membership coefficients of the photovoltaic power generation system output power error corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to the increase in sunlight temperature during time period t.

[0075] Step 102: at the perception layer, calculate the first information benefit value formed by the power Internet of Things providing data collection in the operation monitoring of the photovoltaic power station group; at the network layer, calculate the second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of the photovoltaic power station group; at the network layer, calculate the third information benefit value formed by the power Internet of Things providing data storage and sharing in the operation monitoring of the photovoltaic power station group;

[0076] It should be noted that, at the perception layer, the first information benefit value formed by the data collection provided by the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated, specifically:

[0077] Calculation of information benefit value of the perception layer. In the perception layer, the perception system is used to obtain data information such as sunshine intensity, sunshine time, sunshine shadow, sunshine deflection, and sunshine temperature rise of the photovoltaic power station group to form information benefits. The information benefit value R formed by the power Internet of Things providing data collection in the operation monitoring of the photovoltaic power station group PV1 Calculate according to the following formula:

[0078] R PV1 =E[k Mi (M S +M T +M Y +M A +M ΔT )]

[0079] In the formula, R PV1 The information benefit value generated by the data collection provided by the power Internet of Things to users in the operation monitoring of photovoltaic power station groups; k Mi M S The information benefit value generated by the Internet of Things providing sunshine intensity data collection for photovoltaic power stations at the perception layer, k Mi M T The information benefit value generated by the Internet of Things providing data collection for the sunshine time of photovoltaic power stations at the perception layer, k Mi M Y The information benefit value generated by the Internet of Things providing data collection for the sunshine and shadow of the photovoltaic power station group at the perception layer, k Mi M A The information benefit value formed by providing data collection for the sunshine deflection angle for photovoltaic power stations in the perception layer of the Internet of Things, k Mi M ΔT The information benefit value generated by the Internet of Things providing solar temperature rise data collection for photovoltaic power stations at the perception layer, k Mi is the unit information benefit value brought to users by the Internet of Things providing data collection for photovoltaic power stations at the perception layer; E[] is the expected value of [].

[0080] M S 、M T 、M Y 、M A 、M ΔT It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Where M S The IoT provides data collection for photovoltaic power stations at the perception layer to form a three-dimensional trapezoidal fuzzy set of sunshine intensity. SL 、M SM 、M SU and k MSL , k MSM , k MSU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of sunshine intensity formed by the Internet of Things providing data collection for photovoltaic power stations at the perception layer, M SLj 、M SMj 、M SUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of sunshine intensity formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer; M T The IoT provides data collection for photovoltaic power stations at the perception layer and forms a three-dimensional trapezoidal fuzzy set of sunshine time. TL 、M TM 、M TU and k MTL , k MTM , k MTU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of sunshine time formed by the Internet of Things providing data collection for photovoltaic power stations at the perception layer, M TLj 、M TMj 、M TUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of sunshine time formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer; M Y The three-dimensional trapezoidal fuzzy set of sunshine shadow is formed for the Internet of Things to provide data collection for photovoltaic power stations at the perception layer. YL 、M YM 、M YU and k MYL , k MYM , k MYU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer, M YLj 、M YMj 、M YUj(j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of sunlight shadow formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer; M A The three-dimensional trapezoidal fuzzy set of sunshine deflection angle is formed for the Internet of Things to provide data collection for photovoltaic power stations at the perception layer. AL 、M AM 、M AU and k MAL , k MAM , k MAU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the sunshine deflection angle formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer, M ALj 、M AMj 、M AUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the sunshine deflection angle formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer; M ΔT The IoT provides data collection for photovoltaic power stations at the perception layer, forming a three-dimensional trapezoidal fuzzy set of rising sunshine temperature. ΔTL 、M ΔTM 、M ΔTU and k MΔTL , k MΔTM , k MΔTU The fuzzy sets of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the rising sunshine temperature formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer and their membership coefficients, M ΔTLj 、M ΔTMj 、M ΔTUj (j=1,2,3,4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the sunlight temperature increase formed by the Internet of Things providing data collection for the photovoltaic power station group at the perception layer.

[0088] It should be noted that, at the network layer, the second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power station groups is calculated as follows:

[0089] Calculation of information benefit value at the network layer. At the network layer, the information benefit generated by the power Internet of Things providing data transmission to users. The information benefit value R generated by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power stations PV2 Calculate according to the following formula:

[0090] R PV2 =E[k Dvi v Dvi ]

[0091] In the formula, R PV2The information benefit value generated by the power Internet of Things providing data transmission to users in the operation monitoring of photovoltaic power station groups; k Dvi v Dvi In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission rate of the power Internet of Things.

[0092] It should be noted that, at the network layer, the third information benefit value formed by the power Internet of Things providing data storage and sharing in the operation monitoring of photovoltaic power stations is calculated as follows:

[0093] Calculation of information benefit value at the network layer. At the network layer, the power Internet of Things provides users with information benefits generated by data storage and sharing. The information benefit value R generated by the power Internet of Things providing data storage and sharing in the operation monitoring of photovoltaic power stations PV3 Calculate according to the following formula:

[0094] R PV3 =E[k DSi S Di ]

[0095] In the formula, R PV3 The information benefit value generated by the power Internet of Things providing data storage and sharing to users in the operation monitoring of photovoltaic power station groups; k DSi S Di In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi It is the unit profit value brought to users by the data storage sharing at the i-th rate provided by the power Internet of Things.

[0096] Step 103: Calculate the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group based on the three-dimensional trapezoidal fuzzy set;

[0097] It should be noted that in the perception layer of the power Internet of Things, the perception system is used to collect the fuzzy and uncertain sunshine intensity, sunshine time, sunshine shadow, sunshine deflection, and sunshine temperature rise, which often has errors, which will cause information loss in the operation monitoring of the photovoltaic power station group. The information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated according to the following formula:

[0098]

[0099] Where, L PV It is the information loss value caused by the error in data collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine intensity collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in collecting sunshine intensity of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in sunshine time collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in sunshine time collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine shadow collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in the sunshine shadow collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine deflection angle collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in collecting the sunshine deflection angle of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the collection of the rising sunshine temperature in the sensing system of the power Internet of Things. It is the unit information loss value caused by the error in collecting the sunshine temperature rise of the sensing system in the power Internet of Things.

[0100] Step 104: Calculate the average value of the trust of the power Internet of Things for all users in the power Internet of Things for photovoltaic power station group operation monitoring according to the first information benefit value, the second information benefit value, the third information benefit value and the information loss value;

[0101] It should be noted that in the power Internet of Things for monitoring the operation of photovoltaic power stations, users at the perception layer, network layer and platform layer provide information and benefit users. From this, the average trust of the power Internet of Things for all users can be calculated, and the calculation formula is:

[0102]

[0103] In the formula, B I is the average trust of all users in the power Internet of Things, R PV1 , R PV2 , R PV3 are the first information benefit value, the second information benefit value, and the third information benefit value respectively, L PV is the information loss value.

[0104] Step 105: Evaluate the trust level of the power Internet of Things based on the average trust level of all users of the power Internet of Things.

[0105] It should be noted that the trust level of all users can be evaluated based on the calculated average trust of the power Internet of Things:

[0106] When B I =1, the power Internet of Things is fully suitable for the application of photovoltaic power station group operation monitoring.

[0107] When 0.8≤B I When <1, the power Internet of Things is well adapted to the application of photovoltaic power station group operation monitoring.

[0108] When 0.5≤B I When <0.8, the power Internet of Things is better applied in the operation monitoring of photovoltaic power station groups.

[0109] When 0.3≤B I When <0.5, the power Internet of Things is suitable for the application of photovoltaic power station group operation monitoring.

[0110] When 0≤B I When <0.3, the power Internet of Things cannot adapt to the application of photovoltaic power station group operation monitoring.

[0111] The IoT trust analysis method for photovoltaic power station group operation monitoring of this embodiment adopts a fuzzy probability calculation method for IoT trust for photovoltaic power station group operation monitoring. Its basic principle is that due to the information sufficiency and lack formed by data transmission bandwidth, data transmission rate, and transmission delay, the information sufficiency and lack are used to calculate the IoT trust for photovoltaic power station group operation monitoring, and evaluate the impact of trust on the operation, control and management optimization of photovoltaic power station group.

[0112] The above is an Internet of Things trust analysis method for photovoltaic power station group operation monitoring provided in an embodiment of the present application, and the following is an Internet of Things trust analysis system for photovoltaic power station group operation monitoring provided in an embodiment of the present application.

[0113] See also Figure 2 , an Internet of Things trust analysis system for photovoltaic power station group operation monitoring provided in an embodiment of the present application includes:

[0114] The first calculation unit 201 is used to calculate the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise respectively by using a statistical analysis method according to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise respectively;

[0115] The second calculation unit 202 is used to calculate, at the perception layer, a first information benefit value formed by the power Internet of Things providing data collection in the operation monitoring of the photovoltaic power station group; at the network layer, to calculate a second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of the photovoltaic power station group; at the network layer, to calculate a third information benefit value formed by the power Internet of Things providing data storage and sharing in the operation monitoring of the photovoltaic power station group;

[0116] The third calculation unit 203 is used to calculate the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group based on the three-dimensional trapezoidal fuzzy set;

[0117] The fourth calculation unit 204 is used to calculate the average value of the trust of the power Internet of Things for all users in the power Internet of Things for photovoltaic power station group operation monitoring according to the first information benefit value, the second information benefit value, the third information benefit value and the information loss value;

[0118] The evaluation unit 205 is used to evaluate the trust level of the power Internet of Things according to the average value of the trust of the power Internet of Things to all users.

[0119] Furthermore, an IoT trust analysis device for monitoring the operation of a photovoltaic power station group is provided in an embodiment of the present application, and the device includes a processor and a memory:

[0120] The memory is used to store program code and transmit the program code to the processor;

[0121] The processor is used to execute the steps of the Internet of Things trust analysis method for photovoltaic power station group operation monitoring as described in the above method embodiment according to the instructions in the program code.

[0122] Furthermore, a computer-readable storage medium is provided in an embodiment of the present application, and the computer-readable storage medium is used to store program code, and the program code is used to execute the Internet of Things trust analysis method for photovoltaic power station group operation monitoring described in the above method embodiment.

[0123] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0124] The terms "first", "second", "third", "fourth", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0125] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0126] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An Internet of Things trust analysis method for photovoltaic power station group operation monitoring, characterized in that: include: According to the characteristic relationship between the output power of photovoltaic power generation system and sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise, the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of photovoltaic power generation system and sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise is calculated by statistical analysis method. At the perception layer, the first information benefit value generated by the data collection provided by the power Internet of Things in the operation monitoring of photovoltaic power station groups is calculated; At the network layer, the second information benefit value generated by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power station groups is calculated; At the network layer, the third-party information benefit value generated by the data storage and sharing provided by the power Internet of Things in the operation monitoring of photovoltaic power stations is calculated; Based on the three-dimensional trapezoidal fuzzy set, the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated; Calculate an average value of the trust of the power Internet of Things for all users in the power Internet of Things for photovoltaic power station group operation monitoring according to the first information benefit value, the second information benefit value, the third information benefit value, and the information loss value; The trust level of the power Internet of Things is evaluated based on the average trust of all users in the power Internet of Things. The first information benefit value formed by data collection provided by the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated at the perception layer, specifically including: Based on the calculation formula of the first information benefit value, the first information benefit value generated by the power Internet of Things providing data collection in the operation monitoring of photovoltaic power stations is calculated. ; Wherein, the first information benefit value calculation formula is: ； In the formula, The information benefit value generated by the data collection provided by the power Internet of Things to users in the operation monitoring of photovoltaic power station groups; The information benefit value generated by the Internet of Things providing sunshine intensity data collection for photovoltaic power stations at the perception layer, The information benefit value generated by providing data collection for the sunshine time of photovoltaic power stations at the perception layer of the Internet of Things, The information benefit value generated by providing data collection for the sunlight and shadow of photovoltaic power stations at the perception layer of the Internet of Things, The information benefit value generated by providing data collection of sunshine deflection angle for photovoltaic power stations at the perception layer of the Internet of Things, The information benefit value generated by the Internet of Things providing solar temperature rise data collection for photovoltaic power stations at the perception layer, The unit information benefit value brought to users by the Internet of Things providing data collection for photovoltaic power stations at the perception layer; To find the expected value of []; The calculation of the second information benefit value formed by the power Internet of Things providing data transmission in the operation monitoring of the photovoltaic power station group at the network layer specifically includes: Based on the second information benefit value calculation formula, the second information benefit value generated by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power station groups is calculated. ; Wherein, the second information benefit value calculation formula is: ； In the formula, The information benefit value generated by the power Internet of Things providing data transmission to users in the operation monitoring of photovoltaic power station groups; To provide users with information benefits of data transmission at 9 fuzzy uncertainty rates: very low, very low, low, lower, medium, higher, high, very high, and very high. Provide the first The unit benefit value brought to users by data transmission at a certain rate; At the network layer, the third information benefit value formed by the power Internet of Things providing data storage and sharing in the operation monitoring of the photovoltaic power station group is calculated, specifically including: Based on the third information benefit value calculation formula, the third information benefit value generated by the power Internet of Things providing data storage and sharing in the operation monitoring of photovoltaic power station groups is calculated. ; Wherein, the third information benefit value calculation formula is: ； In the formula, The information benefit value generated by the power Internet of Things providing data storage and sharing to users in the operation monitoring of photovoltaic power station groups; In order to provide users with information benefit values ​​formed by data storage sharing at 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, Provide the first The unit profit value brought to users by data storage sharing at a certain rate.

2. The Internet of Things trust analysis method for photovoltaic power station group operation monitoring according to claim 1 is characterized in that: According to the output power and sunshine intensity of the photovoltaic power generation system, the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error and sunshine intensity of the photovoltaic power generation system is calculated by statistical analysis method, which specifically includes: In the perception layer of the power Internet of Things, the fuzzy and uncertain sunshine intensity is collected through the perception system; Get the user's location during the day from the IoT monitoring data center The data information of sunshine intensity in each period is used to calculate and determine the user period based on the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine intensity using statistical analysis methods. , , three-dimensional trapezoidal fuzzy set of fuzzy uncertainty relationship between photovoltaic power generation system output power error and sunshine intensity : ； ； ； In the formula, , , and , , The time periods The fuzzy numbers and membership coefficients corresponding to the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the output power error of the photovoltaic power generation system related to the sunshine intensity.

3. The Internet of Things trust analysis method for photovoltaic power station group operation monitoring according to claim 2 is characterized in that: The calculating of the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group based on the three-dimensional trapezoidal fuzzy set specifically includes: Substituting the three-dimensional trapezoidal fuzzy set into the information loss value calculation formula, the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group is calculated; The information loss value calculation formula is: ； In the formula, It is the information loss value caused by the error in data collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine intensity collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in collecting sunshine intensity of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in sunshine time collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in sunshine time collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine shadow collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in the sunshine shadow collection of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the sunshine deflection angle collection of the sensing system in the power Internet of Things. It is the unit information loss value caused by the error in collecting the sunshine deflection angle of the sensing system in the power Internet of Things; It is the influence coefficient or weight coefficient formed by the error in the collection of the rising sunshine temperature in the sensing system of the power Internet of Things. It is the unit information loss value caused by the error in collecting the sunshine temperature rise of the sensing system in the power Internet of Things. , , , , They are three-dimensional trapezoidal fuzzy sets of the fuzzy uncertainty relationship between the output power error of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection and sunshine temperature rise.

4. The Internet of Things trust analysis method for photovoltaic power station group operation monitoring according to claim 1 is characterized in that: The average value of the trust of all users of the power Internet of Things for monitoring the operation of the photovoltaic power station group is calculated according to the first information benefit value, the second information benefit value, the third information benefit value and the information loss value, specifically including: Substituting the first information benefit value, the second information benefit value, the third information benefit value and the information loss value into the average value calculation formula, and calculating the average value of the trust of the power Internet of Things for all users in the power Internet of Things for monitoring the operation of the photovoltaic power station group; The average value calculation formula is: ； In the formula, is the average trust level of all users in the power Internet of Things. , , are respectively the first information benefit value, the second information benefit value, and the third information benefit value, is the information loss value.

5. An Internet of Things trust analysis system for photovoltaic power station group operation monitoring, characterized in that: include: The first calculation unit is used to calculate the three-dimensional trapezoidal fuzzy set of the fuzzy uncertainty relationship between the output power error of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection angle and sunshine temperature rise respectively by using a statistical analysis method according to the characteristic relationship between the output power of the photovoltaic power generation system and the sunshine intensity, sunshine time, sunshine shadow, sunshine deflection angle and sunshine temperature rise; The second calculation unit is used to calculate, at the perception layer, a first information benefit value formed by data collection provided by the power Internet of Things in the operation monitoring of the photovoltaic power station group; At the network layer, the second information benefit value generated by the power Internet of Things providing data transmission in the operation monitoring of photovoltaic power station groups is calculated; At the network layer, the third-party information benefit value generated by the data storage and sharing provided by the power Internet of Things in the operation monitoring of photovoltaic power stations is calculated; A third calculation unit is used to calculate the information loss value of the power Internet of Things in the operation monitoring of the photovoltaic power station group based on the three-dimensional trapezoidal fuzzy set; a fourth calculation unit, configured to calculate an average value of the trust of the power Internet of Things for all users in the power Internet of Things for monitoring the operation of the photovoltaic power station group according to the first information benefit value, the second information benefit value, the third information benefit value, and the information loss value; The evaluation unit is used to evaluate the trust level of the power Internet of Things according to the average value of the trust of the power Internet of Things to all users.

6. An IoT trust analysis device for monitoring the operation of a photovoltaic power station group, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the Internet of Things trust analysis method for photovoltaic power station group operation monitoring according to any one of claims 1-4 according to the instructions in the program code.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the Internet of Things trust analysis method for photovoltaic power station group operation monitoring according to any one of claims 1-4.

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