Substation anti-electricity-stealing inspection monitoring method and system based on Beidou technology

Abstract

The invention relates to the technical field of anti-electricity-stealing monitoring, and discloses a transformer substation anti-electricity-stealing inspection monitoring method and system based on the Beidou technology, and the method comprises the steps: deploying an electric power parameter sensor with a Beidou positioning function in a transformer substation power distribution network structure based on an optimized global energy consumption strategy; collecting electric power parameter data of the transformer substation in real time, and preprocessing the electric power parameter data; and constructing an electricity larceny identification model to carry out electricity larceny behavior detection and monitoring video acquisition on the preprocessed electric power parameter data. According to the method, the distance between the electric power parameter sensor and the transformer substation is used as communication energy consumption, deployment position iteration is carried out in combination with historical optimal positions in different triangular network structures, an electric power parameter sensor deployment result with the lowest global energy consumption is obtained, and electric power parameter data acquisition is realized; and the multi-scale characteristics of the power parameter data and the effective neighborhood information representing the change of the power parameter data of the nearest transformer substation are combined to realize electricity stealing behavior detection.

Description

Technical Field

[0001] The present invention relates to the field of anti-electricity theft monitoring, and in particular to a substation anti-electricity theft inspection and monitoring method and system based on Beidou technology. Background Art

[0002] With the continuous growth of electricity demand, the coverage of power network is gradually expanding. As an important part of the power system, the safety and stability of substations are particularly important. However, electricity theft not only threatens the economic interests of power companies, but also may cause potential harm to the stable operation of the power system. Therefore, effective anti-electricity theft inspection and monitoring methods have become an urgent problem to be solved in the power industry. The Beidou satellite navigation system has high positioning accuracy and strong anti-interference ability, which makes the Beidou system have broad application prospects in the power industry, especially in terms of precise positioning, time and space synchronization and safety monitoring, providing solutions for anti-electricity theft inspections. Summary of the invention

[0003] In view of this, the present invention proposes a substation anti-electricity theft inspection and monitoring method based on Beidou technology, aiming to utilize the technical advantages of the Beidou system and combine it with modern information and communication technology to achieve comprehensive monitoring and management of substations and power grids, thereby effectively preventing and combating electricity theft.

[0004] To achieve the above purpose, the present invention provides a substation anti-electricity theft inspection and monitoring method based on Beidou technology, comprising the following steps:

[0005] S1: According to the substation distribution network structure, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure based on the global energy consumption optimization strategy, where the power parameter sensors include current sensors, voltage sensors and temperature sensors;

[0006] S2: Use the deployed power parameter sensors to collect power parameter data of the substation in real time, and transmit the power parameter data to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data;

[0007] S3: In the central monitoring system, a power theft identification model is constructed to detect power theft behavior on the preprocessed power parameter data, wherein abnormal behavior identification by multi-scale feature fusion is an implementation method of the power theft identification model;

[0008] S4: If electricity theft is detected, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area.

[0009] As a further improvement method of the present invention:

[0010] Optionally, obtaining the substation distribution network structure in step S1 includes:

[0011] The Beidou technology is used to obtain the location set of the substation, where the location set of the substation is:

[0012] {L n |n∈[1,N]}

[0013] in:

[0014] L n represents the location of the nth substation, and N represents the total number of substations;

[0015] The substation distribution network structure is constructed based on the substation location set, where the construction process of the substation distribution network structure is as follows:

[0016] Step 1: Initialize and construct a substation triangular network structure set, and the constructed substation triangular network structure set is initially empty;

[0017] Step 2: construct a super triangle network structure containing all substation locations, and put the super triangle network structure into the substation triangle network structure set;

[0018] Step 3: insert the substation locations in the location set into the triangular network structure in the substation triangular network structure set in sequence, and the inserted substation locations are inside any triangular network structure in the substation triangular network structure set;

[0019] The triangular network structure whose circumscribed circle in the triangular network structure set of the substation includes the location of the substation is traversed, and the triangular network structure obtained by traversing constitutes a polygonal structure;

[0020] All triangular network structures inside the polygonal structure are deleted, and the vertices of the polygonal structure are connected with the substation locations to form multiple new triangular network structures;

[0021] Step 4: Repeat step 3 until all substation locations in the location set are inserted into the substation triangle network structure set, so that the super triangle network structure is divided into multiple triangle network structures;

[0022] Step 5: Delete the triangular network structure whose vertices are consistent with the vertices of the super triangular network structure to form a substation distribution network structure, where the substation distribution network structure includes M triangular network structures, and the vertex of each triangular network structure is the substation location;

[0023] Based on the strategy of optimizing global energy consumption, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure.

[0024] Optionally, the deployment of a power parameter sensor with Beidou positioning function in the substation distribution network structure based on the global energy consumption optimization strategy includes:

[0025] Based on the global energy consumption optimization strategy, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure. The power parameter sensors include current sensors, voltage sensors and temperature sensors. The deployment process of power parameter sensors is as follows:

[0026] S11: Obtain M triangular mesh structures, where the vertices of the m-th triangular mesh structure are:

[0027] S12: Construct the power parameter sensor deployment objective function based on the global energy consumption optimization strategy:

[0028]

[0029] θ=(θ(1),θ(2),...,θ(m),...,θ(M))

[0030] in:

[0031] F(θ) represents the power parameter sensor deployment objective function, and θ represents the power parameter sensor deployment result;

[0032] θ(m) represents the deployment position of the power parameter sensor deployed in the mth triangular mesh structure;

[0033] w m Represents the importance weight of the mth triangular mesh structure;

[0034] A m Indicates the annual average power transmission of the power station corresponding to the three vertex positions of the mth triangular grid structure;

[0035] Represents the power parameter sensor deployment position θ(m) and vertex The distance between

[0036] Indicates the communication distance threshold of the power parameter sensor;

[0037] S13: Initialize and generate U groups of power parameter sensor deployment results, wherein the uth group of power parameter sensor deployment results generated by initialization are:

[0038]

[0039] in:

[0040] represents the initialization generated u-th group of power parameter sensor deployment results;

[0041] Indicates the deployment results of power parameter sensors The deployment position of the power parameter sensor deployed in the mth triangular grid structure;

[0042] S14: Set the current iteration number of the power parameter sensor deployment result to t and the maximum iteration number to Max, then the t-th iteration result of the u-th group of power parameter sensor deployment results is The initial value of t is 0;

[0043] S15: Iterate the power parameter sensor deployment results until a preset maximum number of iterations is reached, and substitute the U group of power parameter sensor deployment results obtained in the current iteration into the power parameter sensor deployment objective function, and select the power parameter sensor deployment result with the smallest power parameter sensor deployment objective function value to perform power parameter sensor deployment;

[0044] S16: N substations send information to the nearest power parameter sensor, count the number of messages received by each power parameter sensor, and remove the power parameter sensors that have not received any information.

[0045] Optionally, the step S15 iterates the deployment result of the power parameter sensor, including:

[0046] S151: Calculate and obtain the location information of different power parameter sensor deployment locations, where the power parameter sensor deployment location The location information is:

[0047]

[0048]

[0049] in:

[0050] Indicates the deployment location of the power parameter sensor location information;

[0051] Indicates the deployment locations of power parameter sensors in turn For three vertices The position weight of

[0052] exp(·) represents an exponential function with a natural constant as the base;

[0053] S152: Calculate the fitness of different power parameter sensor deployment locations, where the power parameter sensor deployment location The fitness is:

[0054]

[0055] in:

[0056] Indicates the deployment location of the power parameter sensor Adaptability;

[0057] Record the deployment position of the power parameter sensor with the smallest fitness in different triangular mesh structures during the t iterations as the historical optimal position of the triangular mesh structure, where the historical optimal position of the mth triangular mesh structure after t iterations is the best m (t);

[0058] S153: Iterate the power parameter sensor deployment results, where the power parameter sensor deployment results The iteration formula is:

[0059]

[0060] in:

[0061] rand(-1,1) represents a random number between -1 and 1;

[0062] c represents the control parameter;

[0063] W1 represents the mapping matrix of position information, and T represents transposition;

[0064] represents the historical optimal deployment position sequence after t iterations;

[0065] S154: Let t=t+1, and return to step S151.

[0066] Optionally, in step S2, using the deployed power parameter sensors to collect power parameter data of the substation in real time includes:

[0067] The substation sends the power parameter data to the nearest power parameter sensor, where the power parameter data of the nth substation is:

[0068]

[0069] in:

[0070] I n Indicates the power parameter data of the nth substation;

[0071] represents the current data sequence, voltage data sequence and temperature data sequence of the nth substation in sequence;

[0072] Represents a data sequence G sample data values ​​in, Represents a data sequence The g-th sampled data value in; in this embodiment of the present invention, represents the current value, voltage value and temperature value of the nth substation at the gth sampling moment in sequence;

[0073] The power parameter data is transmitted to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data, where the power parameter data I n Sampling data value The preprocessing formula is:

[0074]

[0075] in:

[0076] Represents the sampled data value The preprocessing results;

[0077] max i Indicates the maximum threshold of the i-th power parameter index, min i represents the minimum threshold value of the ith power parameter indicator; in the embodiment of the present invention, the 1st to 3rd power parameter indicators are current, voltage and temperature, respectively;

[0078] Power parameter data I n The corresponding preprocessing result is x n :

[0079]

[0080] in:

[0081] The current data sequence is Voltage data sequence And the temperature data series The preprocessing results.

[0082] Optionally, transmitting the power parameter data to the central monitoring system via the narrowband Internet of Things includes:

[0083] The power parameter data is transmitted to the central monitoring system through the narrowband Internet of Things, where the power parameter data I n The transmission process is:

[0084] S21: Calculate and obtain the power parameter data I of different power parameter sensors n Transmission capacity:

[0085]

[0086] in:

[0087] R(I n ) represents any power parameter sensor for power parameter data I n transmission capacity;

[0088] B(I n ) represents power parameter data I n The number of transmission bits, byte represents the preset transmission bit threshold;

[0089] E represents the current power of the power parameter sensor;

[0090] DIS(I n ) represents the power parameter sensor and the power parameter data I n The distance between the current locations;

[0091] Dis represents the distance between the power parameter sensor and the central monitoring system, and γ represents the preset transmission distance threshold;

[0092] S22: Power parameter data I n Transmitted to the power parameter sensor with the strongest transmission capability;

[0093] S23: Repeat step S22 until the power parameter data I n Transmitted to the central monitoring system.

[0094] Optionally, in step S3, the electricity theft identification model is constructed to detect electricity theft behavior on the preprocessed power parameter data, including:

[0095] Constructing an electricity theft identification model, wherein the abnormal behavior identification by multi-scale feature fusion is the implementation method of the electricity theft identification model, the electricity theft identification model takes the pre-processed power parameter data as input and takes the electricity theft behavior detection result as output, wherein the electricity theft identification model includes an input layer, a multi-scale feature extraction layer, a neighborhood information extraction layer, a feature fusion layer and a behavior recognition layer;

[0096] The input layer is used to receive pre-processed power parameter data;

[0097] The multi-scale feature extraction layer is a convolutional layer structure in a convolutional neural network, which is used to perform multi-scale convolution operations on the pre-processed power parameter data to form multi-scale power parameter features;

[0098] The neighborhood information extraction layer is a long short-term memory network structure, which is used to extract the effective neighborhood information of the neighboring substation based on the location information of the substation corresponding to the pre-processed power parameter data;

[0099] The feature fusion layer is used to fuse the multi-scale power parameter features and effective neighborhood information to form the power parameter fusion features;

[0100] The behavior recognition layer is a fully connected layer structure in the convolutional neural network, which is used to map the fusion features of power parameters into the identification results of electricity theft behavior;

[0101] The electricity theft identification model is used to detect electricity theft behavior on the preprocessed power parameter data, where the preprocessed power parameter data x n The electricity theft detection process is as follows:

[0102] S31: The input layer receives the pre-processed power parameter data x n ;

[0103] S32: Multi-scale feature extraction layer for pre-processed power parameter data x n Perform multi-scale convolution operations to form multi-scale power parameter features:

[0104]

[0105]

[0106] in:

[0107] f n Represents the pre-processed power parameter data x n Multi-scale power parameter characteristics;

[0108] W k×k represents the convolution weight matrix of k rows and k columns, and * represents the convolution operator;

[0109] Represents the pre-processed power parameter data x n Power parameter characteristics at scale k;

[0110] S33: Neighborhood information extraction layer based on pre-processed power parameter data x n Based on the location information of the corresponding substation, extract the effective neighborhood information of the neighboring substation:

[0111]

[0112] in:

[0113] Q n Indicates the valid neighborhood information of the neighborhood substation;

[0114] Represents x n The pre-processed power parameter data of the substation with the shortest transmission line and the power parameter characteristics at scale K;

[0115] S34: Feature fusion layer for multi-scale power parameter features f n And the effective neighborhood information Q n Perform feature fusion to form power parameter fusion feature Z n :

[0116]

[0117] S35: The behavior recognition layer combines the power parameters with the feature Z n Mapped to the result of electricity theft behavior identification:

[0118]

[0119] in:

[0120] Y n Represents the pre-processed power parameter data x n The corresponding electricity theft behavior identification result; if Y n If it is higher than the preset threshold, it means that electricity theft occurs, otherwise, there is no electricity theft;

[0121] W2 represents the mapping weight matrix.

[0122] Optionally, in step S4, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area, including:

[0123] If the pre-processed power parameter data x is detected n If there is electricity theft, the high-definition camera of the nth substation will be dispatched to collect surveillance video of the substation location area, and anti-electricity theft inspection of the substation will be carried out based on the surveillance video collection results.

[0124] In order to solve the above problems, the present invention provides a substation anti-electricity theft inspection and monitoring system based on Beidou technology, characterized in that the system comprises:

[0125] The sensor deployment module is used to deploy power parameter sensors with Beidou positioning function in the substation distribution network structure based on the global energy consumption optimization strategy according to the substation distribution network structure;

[0126] The data acquisition module is used to collect the power parameter data of the substation in real time using the deployed power parameter sensors, and transmit the power parameter data to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data;

[0127] The electricity theft detection device is used to build an electricity theft identification model to detect electricity theft behavior based on the preprocessed power parameter data. If electricity theft behavior is detected, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area.

[0128] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0129] A memory storing at least one instruction;

[0130] Communication interface, enabling electronic equipment to communicate; and

[0131] The processor executes the instructions stored in the memory to implement the above-mentioned substation anti-electricity theft inspection and monitoring method based on Beidou technology.

[0132] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned substation anti-electricity theft inspection and monitoring method based on Beidou technology.

[0133] Compared with the prior art, the present invention proposes a substation anti-electricity theft inspection and monitoring method based on Beidou technology, which has the following advantages:

[0134] Firstly, this scheme combines the location distribution of substations and divides the substation distribution network structure composed of several triangular network structures, where the vertex of the triangular network structure is the substation location. The distance between the power parameter sensor and the substation is used as the communication energy consumption, and the power parameter sensor deployment objective function is constructed. The deployment position is iterated in combination with the historical optimal position in different triangular network structures to obtain the power parameter sensor deployment result with the lowest global energy consumption. The position of the power parameter sensor is controlled to iterate within the triangular network structure to avoid the close positions of different power parameter sensors, which affects data communication transmission.

[0135] At the same time, this scheme gradually selects the next-hop power parameter sensor for power parameter data transmission according to the distance between different power parameter sensors and the current location of the power parameter data, the sensor power, and the distance to the central monitoring system. It also combines the multi-scale characteristics of the power parameter data and the effective neighborhood information of the nearest substation to perform fusion features to characterize the changes in the power parameter data. Based on the fusion features, power theft detection is achieved. If power theft is detected, the corresponding substation high-definition camera is dispatched to monitor the substation location area for video collection, thereby realizing anti-power theft inspection and monitoring of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] Figure 1A flowchart of a method for anti-electricity theft inspection and monitoring of a substation based on Beidou technology is provided in accordance with an embodiment of the present invention;

[0137] Figure 2 A functional module diagram of a substation anti-electricity theft inspection and monitoring system based on Beidou technology provided by an embodiment of the present invention;

[0138] Figure 2 In: 100 substation anti-electricity theft inspection and monitoring system based on Beidou technology, 101 sensor deployment module, 102 data acquisition module, 103 electricity theft detection device;

[0139] Figure 3 A schematic diagram of the structure of an electronic device for implementing a substation anti-electricity theft inspection and monitoring method based on Beidou technology provided by an embodiment of the present invention.

[0140] Figure 3 In: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface;

[0141] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0142] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0143] The embodiment of the present application provides a substation anti-electricity theft inspection and monitoring method based on Beidou technology. The execution subject of the substation anti-electricity theft inspection and monitoring method based on Beidou technology includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the substation anti-electricity theft inspection and monitoring method based on Beidou technology can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0144] Embodiment 1:

[0145] S1: According to the substation distribution network structure, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure based on the global energy consumption optimization strategy, where the power parameter sensors include current sensors, voltage sensors and temperature sensors.

[0146] The step S1 of obtaining the substation distribution network structure includes:

[0147] The Beidou technology is used to obtain the location set of the substation, where the location set of the substation is:

[0148] {L n |n∈[1,N]}

[0149] in:

[0150] L n represents the location of the nth substation, and N represents the total number of substations;

[0151] The substation distribution network structure is constructed based on the substation location set, where the construction process of the substation distribution network structure is as follows:

[0152] Step 1: Initialize and construct a substation triangular network structure set, and the constructed substation triangular network structure set is initially empty;

[0153] Step 2: construct a super triangle network structure containing all substation locations, and put the super triangle network structure into the substation triangle network structure set;

[0154] Step 3: insert the substation locations in the location set into the triangular network structure in the substation triangular network structure set in sequence, and the inserted substation locations are inside any triangular network structure in the substation triangular network structure set;

[0155] The triangular network structure whose circumscribed circle in the triangular network structure set of the substation includes the location of the substation is traversed, and the triangular network structure obtained by traversing constitutes a polygonal structure;

[0156] All triangular network structures inside the polygonal structure are deleted, and the vertices of the polygonal structure are connected with the substation locations to form multiple new triangular network structures;

[0157] Step 4: Repeat step 3 until all substation locations in the location set are inserted into the substation triangle network structure set, so that the super triangle network structure is divided into multiple triangle network structures;

[0158] Step 5: Delete the triangular network structure whose vertices are consistent with the vertices of the super triangular network structure to form a substation distribution network structure, where the substation distribution network structure includes M triangular network structures, and the vertex of each triangular network structure is the substation location;

[0159] Based on the strategy of optimizing global energy consumption, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure.

[0160] The method of deploying a power parameter sensor with Beidou positioning function in the substation distribution network structure based on the global energy consumption optimization strategy includes:

[0161] Based on the global energy consumption optimization strategy, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure. The power parameter sensors include current sensors, voltage sensors and temperature sensors. The deployment process of power parameter sensors is as follows:

[0162] S11: Obtain M triangular mesh structures, where the vertices of the m-th triangular mesh structure are:

[0163] S12: Construct the power parameter sensor deployment objective function based on the global energy consumption optimization strategy:

[0164]

[0165] θ=(θ(1),θ(2),...,θ(m),...,θ(M))

[0166] in:

[0167] F(θ) represents the power parameter sensor deployment objective function, and θ represents the power parameter sensor deployment result;

[0168] θ(m) represents the deployment position of the power parameter sensor deployed in the mth triangular mesh structure;

[0169] w m Represents the importance weight of the mth triangular mesh structure;

[0170] A m Indicates the annual average power transmission of the power station corresponding to the three vertex positions of the mth triangular grid structure;

[0171] Represents the power parameter sensor deployment position θ(m) and vertex The distance between

[0172] Indicates the communication distance threshold of the power parameter sensor;

[0173] S13: Initialize and generate U groups of power parameter sensor deployment results, wherein the uth group of power parameter sensor deployment results generated by initialization are:

[0174]

[0175] in:

[0176] represents the initialization generated u-th group of power parameter sensor deployment results;

[0177] Indicates the deployment results of power parameter sensors The deployment position of the power parameter sensor deployed in the mth triangular grid structure;

[0178] S14: Set the current iteration number of the power parameter sensor deployment result to t and the maximum iteration number to Max, then the t-th iteration result of the u-th group of power parameter sensor deployment results is The initial value of t is 0;

[0179] S15: Iterate the power parameter sensor deployment results until a preset maximum number of iterations is reached, and substitute the U group of power parameter sensor deployment results obtained in the current iteration into the power parameter sensor deployment objective function, and select the power parameter sensor deployment result with the smallest power parameter sensor deployment objective function value to perform power parameter sensor deployment;

[0180] S16: N substations send information to the nearest power parameter sensor, count the number of messages received by each power parameter sensor, and remove the power parameter sensors that have not received any information.

[0181] The step S15 iterates the deployment result of the power parameter sensor, including:

[0182] S151: Calculate and obtain the location information of different power parameter sensor deployment locations, where the power parameter sensor deployment location The location information is:

[0183]

[0184]

[0185] in:

[0186] Indicates the deployment location of the power parameter sensor location information;

[0187] Indicates the deployment locations of power parameter sensors in turn For three vertices The position weight of

[0188] exp(·) represents an exponential function with a natural constant as the base;

[0189] S152: Calculate the fitness of different power parameter sensor deployment locations, where the power parameter sensor deployment location The fitness is:

[0190]

[0191] in:

[0192] Indicates the deployment location of the power parameter sensor Adaptability;

[0193] Record the deployment position of the power parameter sensor with the smallest fitness in different triangular mesh structures during the t iterations as the historical optimal position of the triangular mesh structure, where the historical optimal position of the mth triangular mesh structure after t iterations is the best m (t);

[0194] S153: Iterate the power parameter sensor deployment results, where the power parameter sensor deployment results The iteration formula is:

[0195]

[0196] in:

[0197] rand(-1,1) represents a random number between -1 and 1;

[0198] c represents the control parameter;

[0199] W1 represents the mapping matrix of position information, and T represents transposition;

[0200] represents the historical optimal deployment position sequence after t iterations;

[0201] S154: Let t=t+1, and return to step S151.

[0202] S2: Use the deployed power parameter sensors to collect the power parameter data of the substation in real time, and transmit the power parameter data to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data.

[0203] In the step S2, the deployed power parameter sensors are used to collect power parameter data of the substation in real time, including:

[0204] The substation sends the power parameter data to the nearest power parameter sensor, where the power parameter data of the nth substation is:

[0205]

[0206] in:

[0207] I n Indicates the power parameter data of the nth substation;

[0208] represents the current data sequence, voltage data sequence and temperature data sequence of the nth substation in sequence;

[0209] Represents a data sequence G sample data values ​​in, Represents a data sequence The g-th sampled data value in; in this embodiment of the present invention, represents the current value, voltage value and temperature value of the nth substation at the gth sampling moment in sequence;

[0210] The power parameter data is transmitted to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data, where the power parameter data I n Sampling data value The preprocessing formula is:

[0211]

[0212] in:

[0213] Represents the sampled data value The preprocessing results;

[0214] max i Indicates the maximum threshold of the i-th power parameter index, min i represents the minimum threshold value of the ith power parameter indicator; in the embodiment of the present invention, the 1st to 3rd power parameter indicators are current, voltage and temperature, respectively;

[0215] Power parameter data I n The corresponding preprocessing result is x n :

[0216]

[0217] in:

[0218] The current data sequence is Voltage data sequence And the temperature data series The preprocessing results.

[0219] The transmission of power parameter data to the central monitoring system through the narrowband Internet of Things includes:

[0220] The power parameter data is transmitted to the central monitoring system through the narrowband Internet of Things, where the power parameter data I n The transmission process is:

[0221] S21: Calculate and obtain the power parameter data I of different power parameter sensors n Transmission capacity:

[0222]

[0223] in:

[0224] R(I n ) represents any power parameter sensor for power parameter data I n transmission capacity;

[0225] B(I n ) represents power parameter data I n The number of transmission bits, byte represents the preset transmission bit threshold;

[0226] E represents the current power of the power parameter sensor;

[0227] DIS(I n ) represents the power parameter sensor and the power parameter data I n The distance between the current locations;

[0228] Dis represents the distance between the power parameter sensor and the central monitoring system, and γ represents the preset transmission distance threshold;

[0229] S22: Power parameter data I n Transmitted to the power parameter sensor with the strongest transmission capability;

[0230] S23: Repeat step S22 until the power parameter data I n Transmitted to the central monitoring system.

[0231] S3: In the central monitoring system, a power theft identification model is constructed to detect power theft behavior based on the preprocessed power parameter data.

[0232] In step S3, a power theft identification model is constructed to detect power theft behavior on the preprocessed power parameter data, including:

[0233] Constructing an electricity theft identification model, wherein the abnormal behavior identification by multi-scale feature fusion is the implementation method of the electricity theft identification model, the electricity theft identification model takes the pre-processed power parameter data as input and takes the electricity theft behavior detection result as output, wherein the electricity theft identification model includes an input layer, a multi-scale feature extraction layer, a neighborhood information extraction layer, a feature fusion layer and a behavior recognition layer;

[0234] The input layer is used to receive pre-processed power parameter data;

[0235] The multi-scale feature extraction layer is a convolutional layer structure in a convolutional neural network, which is used to perform multi-scale convolution operations on the pre-processed power parameter data to form multi-scale power parameter features;

[0236] The neighborhood information extraction layer is a long short-term memory network structure, which is used to extract the effective neighborhood information of the neighboring substation based on the location information of the substation corresponding to the pre-processed power parameter data;

[0237] The feature fusion layer is used to fuse the multi-scale power parameter features and effective neighborhood information to form the power parameter fusion features;

[0238] The behavior recognition layer is a fully connected layer structure in the convolutional neural network, which is used to map the fusion features of power parameters into the identification results of electricity theft behavior;

[0239] The electricity theft identification model is used to detect electricity theft behavior on the preprocessed power parameter data, where the preprocessed power parameter data x n The electricity theft detection process is as follows:

[0240] S31: The input layer receives the pre-processed power parameter data x n ;

[0241] S32: Multi-scale feature extraction layer for pre-processed power parameter data x n Perform multi-scale convolution operations to form multi-scale power parameter features:

[0242]

[0243]

[0244] in:

[0245] f n Represents the pre-processed power parameter data x n Multi-scale power parameter characteristics;

[0246] W k×k represents the convolution weight matrix of k rows and k columns, and * represents the convolution operator;

[0247] Represents the pre-processed power parameter data x n Power parameter characteristics at scale k;

[0248] S33: Neighborhood information extraction layer based on pre-processed power parameter data x n Based on the location information of the corresponding substation, extract the effective neighborhood information of the neighboring substation:

[0249]

[0250] in:

[0251] Q n Indicates the valid neighborhood information of the neighborhood substation;

[0252] Represents x n The pre-processed power parameter data of the substation with the shortest transmission line and the power parameter characteristics at scale K;

[0253] S34: Feature fusion layer for multi-scale power parameter features f n And the effective neighborhood information Q n Perform feature fusion to form power parameter fusion feature Z n :

[0254]

[0255] S35: The behavior recognition layer combines the power parameters with the feature Z n Mapped to the result of electricity theft behavior identification:

[0256]

[0257] in:

[0258] Y n Represents the pre-processed power parameter data x n The corresponding electricity theft behavior identification result; if Y n If it is higher than the preset threshold, it means that electricity theft occurs, otherwise, there is no electricity theft;

[0259] W2 represents the mapping weight matrix.

[0260] S4: If electricity theft is detected, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area.

[0261] In the step S4, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area, including:

[0262] If the pre-processed power parameter data x is detected n If there is electricity theft, the high-definition camera of the nth substation will be dispatched to collect surveillance video of the substation location area, and anti-electricity theft inspection of the substation will be carried out based on the surveillance video collection results.

[0263] Embodiment 2:

[0264] like Figure 2 As shown, it is a functional module diagram of a substation anti-electricity theft inspection and monitoring system based on Beidou technology provided by an embodiment of the present invention, which can implement the substation anti-electricity theft inspection and monitoring method based on Beidou technology in Example 1.

[0265] The substation anti-electricity theft inspection and monitoring system 100 based on Beidou technology of the present invention can be installed in an electronic device. According to the functions to be implemented, the substation anti-electricity theft inspection and monitoring system based on Beidou technology may include a sensor deployment module 101, a data acquisition module 102 and an electricity theft detection device 103. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0266] The sensor deployment module 101 is used to deploy power parameter sensors with Beidou positioning function in the substation distribution network structure based on the global energy consumption optimization strategy according to the substation distribution network structure;

[0267] The data acquisition module 102 is used to collect the power parameter data of the substation in real time using the deployed power parameter sensors, and transmit the power parameter data to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data;

[0268] The electricity theft detection device 103 is used to construct an electricity theft identification model to detect electricity theft behavior on the preprocessed power parameter data. If electricity theft behavior is detected, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area.

[0269] In detail, each module in the substation anti-electricity theft inspection and monitoring system 100 based on Beidou technology in the embodiment of the present invention is used in the same manner as above. Figure 1 The technical means are the same as the substation anti-electricity theft inspection and monitoring method based on Beidou technology described in, and can produce the same technical effects, so I will not go into details here.

[0270] Embodiment 3:

[0271] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a substation anti-electricity theft inspection and monitoring method based on Beidou technology provided by an embodiment of the present invention.

[0272] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0273] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may be used not only to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also to temporarily store data that has been output or is to be output.

[0274] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (such as the program 12 for realizing the anti-electricity theft inspection and monitoring of substations based on Beidou technology), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0275] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0276] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0277] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0278] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0279] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0280] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0281] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0282] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0283] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A substation anti-electricity theft inspection and monitoring method based on Beidou technology, characterized in that: The method comprises: S1: According to the substation distribution network structure, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure based on the global energy consumption optimization strategy, where the power parameter sensors include current sensors, voltage sensors and temperature sensors; S2: Use the deployed power parameter sensors to collect power parameter data of the substation in real time, and transmit the power parameter data to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data; S3: In the central monitoring system, a power theft identification model is constructed to detect power theft behavior based on the pre-processed power parameter data; S4: If electricity theft is detected, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area.

2. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 1, characterized in that: The step S1 of obtaining the substation distribution network structure includes: The Beidou technology is used to obtain the location set of the substation, where the location set of the substation is: {L n |n∈[1,N]} in: L n represents the location of the nth substation, and N represents the total number of substations; The substation distribution network structure is constructed based on the substation location set, where the construction process of the substation distribution network structure is as follows: Step 1: Initialize and construct a substation triangular network structure set, and the constructed substation triangular network structure set is initially empty; Step 2: construct a super triangle network structure containing all substation locations, and put the super triangle network structure into the substation triangle network structure set; Step 3: insert the substation locations in the location set into the triangular network structure in the substation triangular network structure set in sequence, and the inserted substation locations are inside any triangular network structure in the substation triangular network structure set; The triangular network structure whose circumscribed circle in the triangular network structure set of the substation includes the location of the substation is traversed, and the triangular network structure obtained by traversing constitutes a polygonal structure; All triangular network structures inside the polygonal structure are deleted, and the vertices of the polygonal structure are connected with the substation locations to form multiple new triangular network structures; Step 4: Repeat step 3 until all substation locations in the location set are inserted into the substation triangle network structure set, so that the super triangle network structure is divided into multiple triangle network structures; Step 5: Delete the triangular network structure whose vertices are consistent with the vertices of the super triangular network structure to form a substation distribution network structure, where the substation distribution network structure includes M triangular network structures, and the vertex of each triangular network structure is the substation location; Based on the strategy of optimizing global energy consumption, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure.

3. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 2, characterized in that: The method of deploying a power parameter sensor with Beidou positioning function in the substation distribution network structure based on the global energy consumption optimization strategy includes: Based on the global energy consumption optimization strategy, power parameter sensors with Beidou positioning function are deployed in the substation distribution network structure. The power parameter sensors include current sensors, voltage sensors and temperature sensors. The deployment process of power parameter sensors is as follows: S11: Obtain M triangular mesh structures, where the vertices of the m-th triangular mesh structure are: S12: Construct the power parameter sensor deployment objective function based on the global energy consumption optimization strategy: θ=(θ(1),θ(2),...,θ(m),...,θ(M)) in: F(θ) represents the power parameter sensor deployment objective function, and θ represents the power parameter sensor deployment result; θ(m) represents the deployment position of the power parameter sensor deployed in the mth triangular mesh structure; w m Represents the importance weight of the mth triangular mesh structure; A m Indicates the annual average power transmission of the power station corresponding to the three vertex positions of the mth triangular grid structure; Represents the power parameter sensor deployment position θ(m) and vertex The distance between Indicates the communication distance threshold of the power parameter sensor; S13: Initialize and generate U groups of power parameter sensor deployment results, wherein the uth group of power parameter sensor deployment results generated by initialization are: in: represents the initialization generated u-th group of power parameter sensor deployment results; Indicates the deployment results of power parameter sensors The deployment position of the power parameter sensor deployed in the mth triangular grid structure; S14: Set the current iteration number of the power parameter sensor deployment result to t and the maximum iteration number to Max, then the t-th iteration result of the u-th group of power parameter sensor deployment results is The initial value of t is 0; S15: Iterate the power parameter sensor deployment results until a preset maximum number of iterations is reached, and substitute the U group of power parameter sensor deployment results obtained in the current iteration into the power parameter sensor deployment objective function, and select the power parameter sensor deployment result with the smallest power parameter sensor deployment objective function value to perform power parameter sensor deployment; S16: N substations send information to the nearest power parameter sensor, count the number of messages received by each power parameter sensor, and remove the power parameter sensors that have not received any information.

4. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 3, characterized in that: The step S15 iterates the deployment result of the power parameter sensor, including: S151: Calculate and obtain the location information of different power parameter sensor deployment locations, where the power parameter sensor deployment location The location information is: in: Indicates the deployment location of the power parameter sensor location information; Indicates the deployment locations of power parameter sensors in turn For three vertices The position weight of exp(·) represents an exponential function with a natural constant as the base; S152: Calculate the fitness of different power parameter sensor deployment locations, where the power parameter sensor deployment location The fitness is: in: Indicates the deployment location of the power parameter sensor Adaptability; Record the deployment position of the power parameter sensor with the smallest fitness in different triangular mesh structures during the t iterations as the historical optimal position of the triangular mesh structure, where the historical optimal position of the mth triangular mesh structure after t iterations is the best m (t); S153: Iterate the power parameter sensor deployment results, where the power parameter sensor deployment results The iteration formula is: in: rand(-1,1) represents a random number between -1 and 1; c represents the control parameter; W1 represents the mapping matrix of position information, and T represents transposition; represents the historical optimal deployment position sequence after t iterations; S154: Let t=t+1, and return to step S151.

5. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 1, characterized in that: In the step S2, the deployed power parameter sensors are used to collect power parameter data of the substation in real time, including: The substation sends the power parameter data to the nearest power parameter sensor, where the power parameter data of the nth substation is: in: I n Indicates the power parameter data of the nth substation; represents the current data sequence, voltage data sequence and temperature data sequence of the nth substation in sequence; Represents a data sequence G sample data values ​​in, Represents a data sequence The g-th sample data value in ; The power parameter data is transmitted to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data, where the power parameter data I n Sampling data value The preprocessing formula is: in: Represents the sampled data value The preprocessing results; max i Indicates the maximum threshold of the i-th power parameter index, min i represents the minimum threshold of the ith power parameter indicator; Power parameter data I n The corresponding preprocessing result is x n : in: The current data sequence is Voltage data sequence And the temperature data series The preprocessing results.

6. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 5, characterized in that: The transmission of power parameter data to the central monitoring system through the narrowband Internet of Things includes: The power parameter data is transmitted to the central monitoring system through the narrowband Internet of Things, where the power parameter data I n The transmission process is: S21: Calculate and obtain the power parameter data I of different power parameter sensors n Transmission capacity: in: R(I n ) represents any power parameter sensor for power parameter data I n transmission capacity; B(I n ) represents power parameter data I n The number of transmission bits, byte represents the preset transmission bit threshold; E represents the current power of the power parameter sensor; DIS(I n ) represents the power parameter sensor and the power parameter data I n The distance between the current locations; Dis represents the distance between the power parameter sensor and the central monitoring system, and γ represents the preset transmission distance threshold; S22: Power parameter data I n Transmitted to the power parameter sensor with the strongest transmission capability; S23: Repeat step S22 until the power parameter data I n Transmitted to the central monitoring system.

7. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 1, characterized in that: In step S3, a power theft identification model is constructed to detect power theft behavior on the preprocessed power parameter data, including: Constructing an electricity theft identification model, wherein the electricity theft identification model takes the preprocessed power parameter data as input and takes the electricity theft behavior detection result as output, wherein the electricity theft identification model includes an input layer, a multi-scale feature extraction layer, a neighborhood information extraction layer, a feature fusion layer, and a behavior recognition layer; The input layer is used to receive pre-processed power parameter data; The multi-scale feature extraction layer is a convolutional layer structure in a convolutional neural network, which is used to perform multi-scale convolution operations on the pre-processed power parameter data to form multi-scale power parameter features; The neighborhood information extraction layer is a long short-term memory network structure, which is used to extract the effective neighborhood information of the neighboring substation based on the location information of the substation corresponding to the pre-processed power parameter data; The feature fusion layer is used to fuse the multi-scale power parameter features and effective neighborhood information to form the power parameter fusion features; The behavior recognition layer is a fully connected layer structure in the convolutional neural network, which is used to map the fusion features of power parameters into the identification results of electricity theft behavior; The electricity theft identification model is used to detect electricity theft behavior on the preprocessed power parameter data, where the preprocessed power parameter data x n The electricity theft detection process is as follows: S31: The input layer receives the pre-processed power parameter data x n ; S32: Multi-scale feature extraction layer for pre-processed power parameter data x n Perform multi-scale convolution operations to form multi-scale power parameter features: in: f n Represents the pre-processed power parameter data x n Multi-scale power parameter characteristics; W k×k represents the convolution weight matrix of k rows and k columns, and * represents the convolution operator; Represents the pre-processed power parameter data x n Power parameter characteristics at scale k; S33: Neighborhood information extraction layer based on pre-processed power parameter data x n Based on the location information of the corresponding substation, extract the effective neighborhood information of the neighboring substation: in: Q n Indicates the valid neighborhood information of the neighboring substation; Represents x n The pre-processed power parameter data of the substation with the shortest transmission line and the power parameter characteristics at scale K; S34: Feature fusion layer for multi-scale power parameter features f n And the effective neighborhood information Q n Perform feature fusion to form power parameter fusion feature Z n ; S35: The behavior recognition layer combines the power parameters with the feature Z n Mapped to the result of electricity theft behavior identification: in: Y n Represents the pre-processed power parameter data x n The corresponding identification result of electricity theft behavior; if Y n If it is higher than the preset threshold, it means that electricity theft occurs, otherwise, there is no electricity theft; W2 represents the mapping weight matrix.

8. A Beidou technology-based substation anti-electricity theft inspection and monitoring method as claimed in claim 7, characterized in that: In the step S4, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area, including: If the pre-processed power parameter data x is detected n If there is electricity theft, the high-definition camera of the nth substation will be dispatched to collect surveillance video of the substation location area, and anti-electricity theft inspection of the substation will be carried out based on the surveillance video collection results.

9. A substation anti-electricity theft inspection and monitoring system based on Beidou technology, characterized in that: The system comprises: The sensor deployment module is used to deploy power parameter sensors with Beidou positioning function in the substation distribution network structure based on the global energy consumption optimization strategy according to the substation distribution network structure; The data acquisition module is used to collect the power parameter data of the substation in real time using the deployed power parameter sensors, and transmit the power parameter data to the central monitoring system through the narrowband Internet of Things for preprocessing to obtain the preprocessed power parameter data; The electricity theft detection device is used to construct an electricity theft identification model to detect electricity theft behavior on the preprocessed power parameter data. If electricity theft behavior is detected, the corresponding substation high-definition camera is dispatched to collect monitoring video of the substation location area to realize a substation anti-electricity theft inspection and monitoring method based on Beidou technology as described in any one of claims 1-8.

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