Internet of Things trust analysis method and system for energy storage cluster frequency regulation transactions

Abstract

The present application discloses an Internet of Things trust analysis method and system for energy storage cluster frequency modulation transactions, which adopts an Internet of Things trust calculation method for energy storage cluster frequency modulation transactions. The basic principle of this method is: considering the data information formed by data transmission rate and data storage sharing, considering the data errors and missing formed by data collection, and using the information sufficiency and missing amount to calculate the Internet of Things trust for energy storage cluster frequency modulation transactions, and evaluate the impact of trust on energy storage cluster frequency modulation transactions. This Internet of Things trust calculation method for energy storage cluster frequency modulation transactions simultaneously reflects the impact of perception systems, data transmission rates, and data storage sharing, provides theoretical guidance for Internet of Things trust evaluation for energy storage cluster frequency modulation transactions, and provides necessary technical support for frequency modulation transactions based on the 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 system for energy storage cluster frequency modulation transactions. 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, based on the existing Markov chain model, 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, 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 the Internet of Things trust calculation method for energy storage cluster frequency regulation transactions. Therefore, there is an urgent need for an Internet of Things trust analysis method for energy storage cluster frequency regulation transactions to calculate the trust of the power Internet of Things and evaluate the impact of trust on energy storage cluster frequency regulation transactions. Summary of the invention

[0005] The present application provides an Internet of Things trust analysis method and system for energy storage cluster frequency regulation transactions, which are used to calculate the trust of the power Internet of Things and evaluate the impact of the trust on energy storage cluster frequency regulation transactions.

[0006] In view of this, the first aspect of the present application provides an Internet of Things trust analysis method for energy storage cluster frequency modulation transactions, the method comprising:

[0007] According to the frequency regulation price data and frequency regulation power demand data of the power system, as well as the frequency regulation power data and quotation data of the energy storage cluster participating in the frequency regulation, a statistical analysis method is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and frequency regulation power demand of the power system during a certain period, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the energy storage cluster participating in the frequency regulation;

[0008] According to the data transmission rate, data bandwidth data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale.

[0009] The frequency regulation power collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power collection error. The frequency regulation quotation collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation price and quotation collection error.

[0010] According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate, data bandwidth and data storage sharing scale, as well as the information benefit value formed by the data collection provided by the Internet of Things for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer, the information benefit value of the power Internet of Things in frequency regulation trading is calculated;

[0011] According to the frequency regulation power collection error of the energy storage cluster participating in frequency regulation and the frequency regulation quotation collection error of the energy storage cluster participating in frequency regulation, the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated;

[0012] According to the information loss value and the information gain value, the average value of the trust of the power Internet of Things in all users in the power Internet of Things for energy storage cluster frequency regulation transactions is calculated.

[0013] Optionally, the method further includes calculating, based on the information loss and the information gain value, an average value of the trust of the power Internet of Things in all users in the power Internet of Things for energy storage cluster frequency regulation transactions, and then further including:

[0014] 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.

[0015] Optionally, according to the frequency regulation power data of the energy storage cluster participating in the frequency regulation, a statistical analysis method is used to calculate the frequency regulation power of the energy storage cluster participating in the frequency regulation in a certain period, specifically including:

[0016] Using the IoT sensing system, obtain frequency regulation power data information of the energy storage cluster participating in frequency regulation from the power market monitoring data center;

[0017] According to the frequency regulation power data of the energy storage cluster participating in the frequency regulation, the time period t is calculated and determined by using the statistical analysis method, t=1,2,...,N RP , the three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of energy storage clusters participating in frequency regulation

[0018]

[0019] In the formula, and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the energy storage cluster participating in frequency regulation in time period t, 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 frequency regulation power acquisition error of the energy storage cluster participating in frequency regulation in time period t.

[0020] Optionally, according to the data bandwidth data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data bandwidth, specifically including:

[0021] At the network layer of the power Internet of Things, the data bandwidth information is obtained through the Internet of Things monitoring data center, and the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set v of nine fuzzy uncertainties of data bandwidth: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di , i=1,2,...,9:

[0022] B Di =(B DiL ,B DiM ,B DiU )=[(B DiL1 ,B DiL2 ,B DiL3 ,B DiL4 ;k DBiL ),

[0023] (B DiM1 ,B DiM2 ,B DiM3 ,B DiM4 ;k DBiM ),

[0024] (B DiU1 ,B DiU2 ,B DiU3 ,B DiU4 ;k DBiU )];

[0025] In the formula, B Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, B DiL , B DiM , B DiU and k DBiL , k DBiM , k DBiU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the ith three-dimensional trapezoidal fuzzy set of data bandwidth, respectively. DiLj , B DiMj , B DiUj , j = 1, 2, 3, 4, are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data bandwidth respectively.

[0026] Optionally, the three-dimensional trapezoidal fuzzy set calculation of the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things based on the frequency regulation power demand and the frequency regulation power collection error specifically includes:

[0027] The three-dimensional trapezoidal fuzzy set of the frequency regulation power demand of the power system and the frequency regulation power collection error of the energy storage cluster is substituted into the frequency regulation power collection error calculation formula of the energy storage cluster, and the frequency regulation power collection error of the West-to-East Power Transmission Units involved in the frequency regulation formed in the power Internet of Things is calculated.

[0028] The frequency modulation power acquisition error calculation formula is:

[0029]

[0030] In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power demand, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power quantity, Represents the union of fuzzy sets.

[0031] Optionally, the information benefit value of the power Internet of Things in frequency regulation trading is calculated based on a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale, and the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer, specifically including:

[0032] Substitute the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale, as well as the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer into the information benefit value calculation formula, and calculate the information benefit value of the power Internet of Things in frequency regulation trading;

[0033] The information benefit value calculation formula is:

[0034]

[0035] In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; 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 of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value formed by the data bandwidth of very low, very low, low, lower, medium, higher, high, very high, and very high fuzzy uncertainty rates, k DBi is the unit profit value brought to users by the data bandwidth of the ith rate provided by the power Internet of Things; 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 k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage cluster at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, kMi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. Represents the union of 9 fuzzy sets.

[0036] Optionally, the frequency regulation power amount and quotation collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction according to the frequency regulation power amount collection error of the participating frequency regulation energy storage cluster and the frequency regulation quotation collection error of the participating frequency regulation energy storage cluster, specifically including:

[0037] Substitute the frequency regulation power collection error of the frequency regulation energy storage cluster and the frequency regulation quotation collection error of the frequency regulation energy storage cluster into the information loss value calculation formula, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things;

[0038] Wherein, the information loss value calculation formula is:

[0039]

[0040] Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation energy storage clusters formed in the power Internet of Things will cause information loss value of frequency regulation transactions; It is the influence coefficient or weight coefficient of the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation energy storage cluster, Collect frequency quotation errors for frequency regulation energy storage clusters.

[0041] Optionally, the calculating, according to the information loss value and the information gain value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for energy storage cluster frequency regulation transactions specifically includes:

[0042] Substituting the information loss value and the information gain value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for energy storage cluster frequency regulation transactions is calculated;

[0043] Wherein, the average value calculation formula is:

[0044]

[0045] In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RP is the information loss value.

[0046] A second aspect of the present application provides an Internet of Things trust analysis system for energy storage cluster frequency modulation transactions, the system comprising:

[0047] The first calculation unit is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in a certain period of time, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and the quotation of the energy storage cluster participating in the frequency regulation by using a statistical analysis method according to the frequency regulation price data and the frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the energy storage cluster participating in the frequency regulation;

[0048] The second calculation unit is used to calculate and determine the data transmission rate, data bandwidth, and data storage sharing scale data of the Internet of Things monitoring data center by using a statistical analysis method, respectively, a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth, and data storage sharing scale;

[0049] The third calculation unit is used to calculate the frequency regulation power collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the collection error of the frequency regulation power, and calculate the frequency regulation quotation collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the collection error of the frequency regulation price and quotation;

[0050] The fourth calculation unit is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a number of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth and data storage sharing scale, and the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer;

[0051] A fifth calculation unit is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage clusters involved in the power Internet of Things according to the frequency regulation power collection error of the frequency regulation energy storage clusters involved in the frequency regulation and the frequency quotation collection error of the frequency regulation energy storage clusters involved in the frequency regulation;

[0052] The sixth calculation unit 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 energy storage cluster frequency regulation transactions based on the information loss value and the information gain value.

[0053] Optionally, it further comprises: an evaluation unit;

[0054] The evaluation unit is used to evaluate the trust level of the power Internet of Things based on the average value of the trust of all users of the power Internet of Things.

[0055] It can be seen from the above technical solutions that this application has the following advantages:

[0056] Through the IoT trust analysis method for energy storage cluster frequency modulation transactions proposed in this application, the IoT trust for energy storage cluster frequency modulation transactions can be calculated. This IoT trust calculation method for energy storage cluster frequency modulation transactions simultaneously reflects the influence of the perception system, data transmission rate, and data storage sharing, provides theoretical guidance for IoT trust evaluation for energy storage cluster frequency modulation transactions, and provides necessary technical support for frequency modulation transactions based on the IoT. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of an Internet of Things trust analysis method for energy storage cluster frequency modulation transactions provided in an embodiment of the present application;

[0058] Figure 2 This is a structural diagram of an Internet of Things trust analysis system for energy storage cluster frequency modulation trading provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] 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.

[0060] See also Figure 1 , an Internet of Things trust analysis method for energy storage cluster frequency modulation transactions provided in an embodiment of the present application includes:

[0061] Step 101: According to the frequency regulation price data and frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the energy storage cluster participating in the frequency regulation, a statistical analysis method is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and frequency regulation power demand of the power system in a certain period, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the energy storage cluster participating in the frequency regulation;

[0062] It should be noted that step 101 specifically includes:

[0063] 1011. Use the IoT sensing system to obtain relevant data information on the power system frequency regulation price from the power market monitoring data center. According to the data on the power system frequency regulation price obtained from the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N e , N e The three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system (time period number)

[0064]

[0065] In the formula, and are the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t or the fuzzy numbers and membership coefficients 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 frequency regulation price collection error of the power system in time period t.

[0066] 1012. Use the IoT sensing system to obtain relevant data information on the power demand for power system frequency regulation from the power market monitoring data center. According to the data on the power demand for power system frequency regulation obtained from the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set for power system frequency regulation power demand acquisition error

[0067]

[0068] In the formula and are the three-dimensional trapezoidal fuzzy set of the power demand for frequency regulation of the power system during period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, 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 frequency regulation power demand acquisition error of the power system in time period t.

[0069] 1013. Use the IoT sensing system to obtain the frequency modulation power data of the energy storage cluster participating in the frequency modulation from the power market monitoring data center. According to the frequency modulation power data of the energy storage cluster participating in the frequency modulation in the power market, a statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of energy storage cluster participating in frequency regulation

[0070]

[0071] In the formula and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the energy storage cluster participating in frequency regulation in time period t, 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 frequency regulation power acquisition error of the energy storage cluster participating in frequency regulation in time period t.

[0072] 1014. Using the IoT sensing system, obtain the relevant data information of the quotations of the energy storage clusters participating in the frequency regulation from the power market monitoring data center. According to the quotations of the energy storage clusters participating in the frequency regulation in the power market, a statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP ) Three-dimensional trapezoidal fuzzy set of the quotation collection error of the energy storage cluster participating in frequency regulation

[0073]

[0074] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the energy storage cluster participating in frequency regulation in time period t, or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, 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 quotation collection error of the energy storage cluster participating in frequency regulation during time period t.

[0075] Step 102: According to the data transmission rate, data bandwidth data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to respectively calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale;

[0076] It should be noted that step 102 specifically includes:

[0077] 1021. In the network layer of the power Internet of Things, relevant data information on data transmission rate is obtained from the Internet of Things monitoring data center, and statistical analysis methods are used to calculate and determine the three-dimensional trapezoidal fuzzy sets v of nine fuzzy uncertainties of data transmission rate: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high Di (i=1,2,...,9):

[0078] v Di =(v DiL ,v DiM ,v DiU )=[(v DiL1 ,v DiL2 ,v DiL3 ,v DiL4 ;k DviL ),

[0079] (v DiM1 ,v DiM2 ,v DiM3 ,v DiM4 ;k DviM ),

[0080] (v DiU1 ,v DiU2 ,v DiU3 ,v DiU4 ;k DviU )]

[0081] In the formula, v Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, v DiL 、v DiM 、v DiU and k DviL , k DviM , k DviU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate, respectively. DiLj 、v DiMj 、v DiUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate.

[0082] 1022. In the network layer of the power Internet of Things, relevant data information of data bandwidth is obtained from the Internet of Things monitoring data center, and statistical analysis methods are used to calculate and determine the three-dimensional trapezoidal fuzzy sets v of 9 fuzzy uncertainties of data bandwidth: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high Di (i=1,2,...,9):

[0083] B Di =(B DiL ,B DiM ,B DiU )=[(B DiL1 ,B DiL2 ,B DiL3 ,B DiL4 ;k DBiL ),

[0084] (B DiM1 ,B DiM2 ,B DiM3 ,B DiM4 ;k DBiM ),

[0085] (B DiU1 ,B DiU2 ,B DiU3 ,B DiU4 ;k DBiU )]

[0086] In the formula, B Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, B DiL , B DiM , B DiU and k DBiL , k DBiM , k DBiU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the ith three-dimensional trapezoidal fuzzy set of data bandwidth, respectively. DiLj , B DiMj , B DiUj (j=1,2,3,4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data bandwidth respectively.

[0087] 1023. At the platform level of the power Internet of Things, relevant data information on the scale of data storage sharing is obtained from the Internet of Things monitoring data center, and a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set S of nine fuzzy uncertainties: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di (i=1,2,...,9):

[0088] S Di =(S DiL ,S DiM ,SDiU )=[(S DiL1 ,S DiL2 ,S DiL3 ,S DiL4 ;k DSiL ),

[0089] (S DiM1 ,S DiM2 ,S DiM3 ,S DiM4 ;k DSiM ),

[0090] (S DiU1 ,S DiU2 ,S DiU3 ,S DiU4 ;k DSiU )]

[0091] In the formula, S Di is the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiL , S DiM , S DiU and k DSiL , k DSiM , k DSiU They are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiLj , S DiMj , S DiUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data storage sharing scale respectively.

[0092] Step 103, the frequency modulation power collection error of the participating frequency modulation energy storage cluster formed in the power Internet of Things is calculated according to the three-dimensional trapezoidal fuzzy set of the frequency modulation power demand and the collection error of the frequency modulation power, and the frequency modulation quotation collection error of the participating frequency modulation energy storage cluster formed in the power Internet of Things is calculated according to the three-dimensional trapezoidal fuzzy set of the collection error of the frequency modulation price and quotation;

[0093] It should be noted that the frequency regulation power collection error of the energy storage cluster participating in the frequency regulation is calculated based on the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the frequency regulation power collection error, which specifically includes:

[0094] The frequency modulation power collection error of the energy storage cluster participating in the frequency modulation formed in the power Internet of Things is jointly determined by the frequency modulation power demand of the power system and the frequency modulation power collection error of the energy storage cluster. The calculation is as follows:

[0095]

[0096] In the formula, Represents the union of fuzzy sets.

[0097] It should be noted that the frequency regulation quotation collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of the frequency regulation price and quotation collection error, specifically including:

[0098] The frequency regulation quotation collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is jointly determined by the power system frequency regulation power demand and the frequency regulation quotation collection error of the energy storage cluster. The calculation is as follows:

[0099]

[0100] Step 104, according to a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data transmission rate, data bandwidth and data storage sharing scale, and the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer, calculate the information benefit value of the power Internet of Things in frequency regulation trading;

[0101] It should be noted that at the perception layer, the perception system is used to obtain data information such as electricity market prices, power demand for power system frequency regulation and its quotation, and frequency regulation power of various generator sets and their quotation. At the platform layer, with the help of the big data system, the sharing and interaction of power system frequency regulation data can be realized. At the network layer, with the help of the network system, data transmission can be realized, so that frequency regulation users can use the Internet of Things to obtain sufficient information and gain benefits. The information benefit value R of the power Internet of Things in frequency regulation transactions RP Calculate according to the following formula:

[0102]

[0103] In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; 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 of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value formed by the data bandwidth of very low, very low, low, lower, medium, higher, high, very high, and very high fuzzy uncertainty rates, k DBiis the unit profit value brought to users by the data bandwidth of the ith rate provided by the power Internet of Things; 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 k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the IoT providing data collection for the energy storage cluster at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for the energy storage cluster at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage cluster at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. Represents the union of 9 fuzzy sets.

[0104] M H 、M T 、M N 、M G 、M ES 、M X It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows:

[0105] M H =(M HL ,M HM ,M HU )=[(M HL1 ,M HL2 ,M HL3 ,M HL4 ;k HL ),

[0106] (M HM1 ,M HM2 ,M HM3 ,M HM4 ;k HM ),

[0107] (M HU1 ,M HU2 ,M HU3 ,M HU4 ;k HU )]

[0108] M T =(M TL ,M TM ,M TU )=[(M TL1 ,M TL2 ,M TL3 ,M TL4 ;k HL ),

[0109] (M TM1 ,M TM2 ,M TM3 ,M TM4 ;k TM ),

[0110] (M TU1 ,M TU2 ,M TU3 ,M TU4 ;k TU )]

[0111] M N =(M NL ,M NM ,M NU )=[(M NL1 ,M NL2 ,M NL3 ,M NL4 ;k NL ),

[0112] (M NM1 ,M NM2 ,M NM3 ,M NM4 ;k NM ),

[0113] (M NU1 ,M NU2 ,M NU3 ,M NU4 ;k NU )]

[0114] M G =(M GL ,M GM ,M GU )=[(M GL1 ,M GL2 ,M GL3 ,M GL4 ;k GL ),

[0115] (M GM1 ,M GM2 ,M GM3 ,M GM4 ; k GM ),

[0116] (M GU1 ,M GU2 ,M GU3 ,M GU4 ; k GU )]

[0117] M ES = (M ESL ,M ESM ,M ESU ) = [(M ESL1 ,M ESL2 ,M ESL3 ,M ESL4 ; k ESL ),

[0118] (M ESM1 ,M ESM2 ,M ESM3 ,M ESM4 ; k ESM ),

[0119] (M ESU1 ,M ESU2 ,M ESU3 ,M ESU4 ; k ESU )]

[0120] M X = (M XL ,M XM ,M XU ) = [(M XL1 ,M XL2 ,M XL3 ,M XL4 ; k XL ),

[0121] (M XM1 ,M XM2 ,M XM3 ,M XM4 ; k XM ),

[0122] (M XU1 ,M XU2 ,M XU3 ,M XU4 ; k XU )]

[0123] In the formula, M HThe three-dimensional trapezoidal fuzzy set of the data collection scale provided by the IoT for the energy storage cluster at the perception layer, M HL 、M HM 、M HU and k HL , k HM , k HU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the energy storage cluster at the perception layer, respectively. HLj 、M HMj 、M HUj (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 data collection scale provided by the Internet of Things for the energy storage cluster at the perception layer; M T The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the IoT for the energy storage cluster at the perception layer, M TL 、M TM 、M TU and k TL , k TM , k TU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the energy storage cluster at the perception layer, respectively. 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 the data collection scale provided by the Internet of Things for the energy storage cluster at the perception layer; M N The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for nuclear power units at the perception layer, M NL 、M NM 、M NU and k NL , k NM , k NU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for nuclear power units at the perception layer, respectively. NLj 、M NMj 、M NUj (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 data collection scale provided by the Internet of Things for nuclear power units at the perception layer; M G The three-dimensional trapezoidal fuzzy set of data collection scale is provided for gas-fired units in the perception layer of the Internet of Things. GL 、M GM 、M GU and k GL , k GM , k GUThe fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the gas-fired unit in the perception layer. GLj 、M GMj 、M GUj (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 data collection scale provided by the Internet of Things for the gas-fired generator set at the perception layer; M ES The three-dimensional trapezoidal fuzzy set of the data collection scale is provided for the energy storage motor group in the perception layer of the Internet of Things. ESL 、M ESM 、M ESU and k ESL , k ESM , k ESU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the energy storage motor group in the perception layer. ESLj 、M ESMj 、M ESUj (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 data collection scale provided by the Internet of Things for the energy storage generator group at the perception layer; M X The three-dimensional trapezoidal fuzzy set of the data collection scale is provided for the West-East Power Transmission Unit in the perception layer of the Internet of Things. XL 、M XM 、M XU and k XL , k XM , k XU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the data collection scale of the West-East Power Transmission Unit at the perception layer, M XLj 、M XMj 、M XUj (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 data collection scale provided by the Internet of Things at the perception layer for the West-East Power Transmission Units.

[0124] Step 105: Calculate the information loss value of frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage clusters formed in the power Internet of Things according to the frequency regulation power collection error of the frequency regulation energy storage clusters and the frequency regulation quotation collection error of the frequency regulation energy storage clusters;

[0125] It should be noted that the frequency regulation power and quotation collection errors of the frequency regulation energy storage cluster formed in the power Internet of Things will cause information loss in frequency regulation transactions, and its value is calculated according to the following formula:

[0126]

[0127] Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation energy storage clusters formed in the power Internet of Things will cause information loss value of frequency regulation transactions; It is the influence coefficient or weight coefficient of the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things.

[0128] Step 106: Calculate the average trust of the power Internet of Things for all users in the power Internet of Things for energy storage cluster frequency regulation transactions based on the information loss value and the information gain value.

[0129] It should be noted that in the power Internet of Things for energy storage cluster frequency regulation transactions, 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:

[0130]

[0131] In one embodiment, the IoT trust analysis method for energy storage cluster frequency modulation transactions of the present application further includes:

[0132] 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.

[0133] 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:

[0134] When B I =1, the power Internet of Things is fully adapted to the application of energy storage cluster frequency regulation trading.

[0135] When 0.8≤B I When <1, the power Internet of Things is well adapted to the application of energy storage cluster frequency regulation trading.

[0136] When 0.5≤B I When <0.8, the power Internet of Things is better adapted to the application of energy storage cluster frequency regulation trading.

[0137] When 0.3≤B I When <0.5, the power Internet of Things is suitable for the application of energy storage cluster frequency regulation trading.

[0138] When 0≤BI When <0.3, the power Internet of Things cannot adapt to the application of energy storage cluster frequency regulation trading.

[0139] The IoT trust analysis method for energy storage cluster frequency regulation transactions provided in the embodiment of the present application adopts an IoT trust calculation method for energy storage cluster frequency regulation transactions. Its basic principle is: considering the data information formed by data transmission rate and data storage sharing, considering the data errors and missing formed by data collection, using the information sufficiency and missing amount to calculate the IoT trust for energy storage cluster frequency regulation transactions, and evaluating the impact of trust on energy storage cluster frequency regulation transactions.

[0140] The above is an Internet of Things trust analysis method for energy storage cluster frequency modulation transactions provided in an embodiment of the present application, and the following is an Internet of Things trust analysis system for energy storage cluster frequency modulation transactions provided in an embodiment of the present application.

[0141] See also Figure 2 , an Internet of Things trust analysis system for energy storage cluster frequency modulation transactions provided in an embodiment of the present application includes:

[0142] The first calculation unit 201 is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in a certain period of time, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and the quotation of the energy storage cluster participating in the frequency regulation by using a statistical analysis method according to the frequency regulation price data and the frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the energy storage cluster participating in the frequency regulation;

[0143] The second calculation unit 202 is used to calculate and determine the data transmission rate, data bandwidth, and data storage sharing scale data of the Internet of Things monitoring data center by using a statistical analysis method, respectively, a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth, and data storage sharing scale;

[0144] The third calculation unit 203 is used to calculate the frequency regulation power collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the collection error of the frequency regulation power, and calculate the frequency regulation quotation collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the collection error of the frequency regulation price and quotation;

[0145] The fourth calculation unit 204 is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth and data storage sharing scale, and the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer;

[0146] The fifth calculation unit 205 is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things according to the frequency regulation power collection error of the frequency regulation energy storage cluster and the frequency regulation quotation collection error of the frequency regulation energy storage cluster;

[0147] The sixth calculation unit 206 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 energy storage cluster frequency regulation transactions according to the information loss value and the information gain value.

[0148] In one embodiment, the IoT trust analysis system for energy storage cluster frequency modulation transactions of the present application further includes:

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 energy storage cluster frequency modulation transactions, characterized in that: include: According to the frequency regulation price data and frequency regulation power demand data of the power system, as well as the frequency regulation power data and quotation data of the energy storage cluster participating in the frequency regulation, a statistical analysis method is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and frequency regulation power demand of the power system during a certain period, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the energy storage cluster participating in the frequency regulation; According to the data transmission rate, data bandwidth data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale. The frequency regulation power collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power collection error. The frequency regulation quotation collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation price and quotation collection error. According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate, data bandwidth and data storage sharing scale, as well as the information benefit value formed by the data collection provided by the Internet of Things for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer, the information benefit value of the power Internet of Things in frequency regulation trading is calculated; According to the frequency regulation power collection error of the energy storage cluster participating in frequency regulation and the frequency regulation quotation collection error of the energy storage cluster participating in frequency regulation, the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the energy storage cluster participating in frequency regulation formed in the power Internet of Things is calculated; According to the information loss value and the information gain value, calculating the average trust of the power Internet of Things for all users in the power Internet of Things for energy storage cluster frequency regulation transactions; in: The three-dimensional trapezoidal fuzzy set calculation of the frequency modulation power collection error of the frequency modulation power demand and the frequency modulation power collection error formed in the power Internet of Things specifically includes: The three-dimensional trapezoidal fuzzy set of the frequency regulation power demand of the power system and the frequency regulation power collection error of the energy storage cluster is substituted into the frequency regulation power collection error calculation formula of the energy storage cluster, and the frequency regulation power collection error of the West-to-East Power Transmission Units involved in the frequency regulation formed in the power Internet of Things is calculated. The frequency modulation power acquisition error calculation formula is: In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power demand, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power quantity, represents the union of fuzzy sets; The information benefit value of the power Internet of Things in frequency regulation trading is calculated based on the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale, and the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer, specifically including: Substitute the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale, as well as the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer into the information benefit value calculation formula, and calculate the information benefit value of the power Internet of Things in frequency regulation trading; The information benefit value calculation formula is: In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; 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 of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value formed by the data bandwidth of extremely low, very low, low, relatively low, medium, relatively high, very high and extremely high 9 fuzzy uncertainty rates, k DBi is the unit profit value brought to users by the data bandwidth of the ith rate provided by the power Internet of Things; 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 k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage cluster at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. represents the union of 9 fuzzy sets; The frequency regulation power amount and quotation collection error of the frequency regulation energy storage cluster participating in the frequency regulation is calculated based on the frequency regulation power amount and quotation collection error of the frequency regulation energy storage cluster participating in the frequency regulation, which will cause the information loss value of the frequency regulation transaction in the power Internet of Things, specifically including: Substitute the frequency regulation power collection error of the frequency regulation energy storage cluster and the frequency regulation quotation collection error of the frequency regulation energy storage cluster into the information loss value calculation formula, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things; The information loss value calculation formula is: Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation energy storage clusters formed in the power Internet of Things will cause information loss value of frequency regulation transactions; It is the influence coefficient or weight coefficient of the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation energy storage cluster, Collect frequency quotation errors for frequency regulation energy storage clusters.

2. The Internet of Things trust analysis method for energy storage cluster frequency modulation transactions according to claim 1 is characterized in that: The method further includes calculating the average trust of all users of the power Internet of Things in the power Internet of Things for energy storage cluster frequency regulation transactions based on the information loss and the information gain value, and then further including: 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.

3. The Internet of Things trust analysis method for energy storage cluster frequency modulation transactions according to claim 1 is characterized in that: According to the frequency regulation power data of the energy storage cluster participating in frequency regulation, the frequency regulation power of the energy storage cluster participating in frequency regulation in a certain period is calculated by using statistical analysis method, specifically including: Using the IoT sensing system, obtain frequency regulation power data information of the energy storage cluster participating in frequency regulation from the power market monitoring data center; According to the frequency regulation power data of the energy storage cluster participating in the frequency regulation, the time period t is calculated and determined by using the statistical analysis method, t=1,2,...,N RP , the three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of energy storage clusters participating in frequency regulation In the formula, is the three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of the energy storage cluster participating in frequency regulation in period t, are the fuzzy numbers of the lower, middle and upper bound fuzzy sets of the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set of the energy storage cluster participating in frequency regulation in period t, j = 1, 2, 3, 4, They are the membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set.

4. The Internet of Things trust analysis method for energy storage cluster frequency modulation transactions according to claim 1 is characterized in that: According to the data bandwidth data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine several three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data bandwidth, including: At the network layer of the power Internet of Things, the data bandwidth information is obtained through the Internet of Things monitoring data center, and the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set v of nine fuzzy uncertainties of data bandwidth: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di , i=1,2,...,9: B Di =(B DiL ,B DiM ,B DiU )=[(B DiL1 ,B DiL2 ,B DiL3 ,B DiL4 ;k DBiL ), (B DiM1 ,B DiM2 ,B DiM3 ,B DiM4 ;k DBiM ), (B DiU1 ,B DiU2 ,B DiU3 ,B DiU4 ;k DBiU )]; In the formula, B Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, B DiL , B DiM , B DiU and k DBiL , k DBiM , k DBiU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the ith three-dimensional trapezoidal fuzzy set of data bandwidth, respectively. DiLj , B DiMj , B DiUj , j = 1, 2, 3, 4, are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data bandwidth respectively.

5. The Internet of Things trust analysis method for energy storage cluster frequency modulation transactions according to claim 1 is characterized in that: The calculation of the average trust of the power Internet of Things for all users in the power Internet of Things for energy storage cluster frequency regulation transactions based on the information loss value and the information gain value specifically includes: Substituting the information loss value and the information gain value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for energy storage cluster frequency regulation transactions is calculated; Wherein, the average value calculation formula is: In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RP is the information loss value.

6. An Internet of Things trust analysis system for energy storage cluster frequency modulation transactions, characterized in that: include: The first calculation unit is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in a certain period of time, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and the quotation of the energy storage cluster participating in the frequency regulation by using a statistical analysis method according to the frequency regulation price data and the frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the energy storage cluster participating in the frequency regulation; The second calculation unit is used to calculate and determine the data transmission rate, data bandwidth, and data storage sharing scale data of the Internet of Things monitoring data center by using a statistical analysis method, respectively, a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth, and data storage sharing scale; The third calculation unit is used to calculate the frequency regulation power collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the collection error of the frequency regulation power, and calculate the frequency regulation quotation collection error of the participating frequency regulation energy storage cluster formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the collection error of the frequency regulation price and quotation; The fourth calculation unit is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a number of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth and data storage sharing scale, and the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer; A fifth calculation unit is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage clusters involved in the power Internet of Things according to the frequency regulation power collection error of the frequency regulation energy storage clusters involved in the frequency regulation and the frequency quotation collection error of the frequency regulation energy storage clusters involved in the frequency regulation; a sixth calculation unit, configured to calculate, according to the information loss value and the information gain value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for energy storage cluster frequency regulation transactions; in: The third computing unit is specifically used for: The three-dimensional trapezoidal fuzzy set of the frequency regulation power demand of the power system and the frequency regulation power collection error of the energy storage cluster is substituted into the frequency regulation power collection error calculation formula of the energy storage cluster, and the frequency regulation power collection error of the West-to-East Power Transmission Units involved in the frequency regulation formed in the power Internet of Things is calculated. The frequency modulation power acquisition error calculation formula is: In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power demand, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power quantity, represents the union of fuzzy sets; The fourth computing unit is specifically configured to: Substitute the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth and data storage sharing scale, as well as the information benefit value formed by the Internet of Things providing data collection for energy storage clusters, thermal power units, nuclear power units, gas power units, energy storage clusters and west-to-east power transmission units at the perception layer into the information benefit value calculation formula, and calculate the information benefit value of the power Internet of Things in frequency regulation trading; The information benefit value calculation formula is: In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; 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 of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value formed by the data bandwidth of extremely low, very low, low, relatively low, medium, relatively high, high, very high and extremely high 9 fuzzy uncertainty rates, k DBi is the unit profit value brought to users by the data bandwidth of the ith rate provided by the power Internet of Things; 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 k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M H The information benefit value generated by the Internet of Things providing data collection for hydropower units at the perception layer, k Mi M T The information benefit value generated by the IoT providing data collection for thermal power units at the perception layer, k Mi M N The information benefit value generated by the Internet of Things providing data collection for nuclear power units at the perception layer, k Mi M G The information benefit value generated by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer, k Mi M ES The information benefit value generated by the IoT providing data collection for the energy storage cluster at the perception layer, k Mi M X The information benefit value generated by the Internet of Things providing data collection for the West-to-East Power Transmission Unit at the perception layer, k Mi is the unit benefit value brought to users by the Internet of Things providing data collection for the unit at the perception layer; E[] is the expected value of []. The fifth calculating unit representing the union of nine fuzzy sets is specifically used for: Substitute the frequency regulation power collection error of the frequency regulation energy storage cluster and the frequency regulation quotation collection error of the frequency regulation energy storage cluster into the information loss value calculation formula, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things; The information loss value calculation formula is: Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation energy storage clusters formed in the power Internet of Things will cause information loss value of frequency regulation transactions; It is the influence coefficient or weight coefficient of the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation energy storage cluster formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation energy storage cluster formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation energy storage cluster, Collect frequency quotation errors for frequency regulation energy storage clusters.

7. The Internet of Things trust analysis system for energy storage cluster frequency modulation transactions according to claim 6 is characterized in that: Also includes: Assessment Unit; The evaluation unit is used to evaluate the trust level of the power Internet of Things based on the average value of the trust of all users of the power Internet of Things.

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