Park power monitoring method, device, system, equipment and medium

Abstract

The invention relates to a park power monitoring method and device, equipment and a medium, and effectively solves the technical problems that the cost is too high due to numerous buildings in a park, a huge power system structure and a huge related equipment level data size, information islands exist between the buildings, information linkage is lacked, and the power monitoring efficiency is influenced. The park electric power monitoring method comprises the following steps: acquiring real-time operation data of a park and an electric power system digital twinborn model corresponding to the park; inputting the real-time operation data into a fault diagnosis model, and determining a fault result corresponding to the real-time operation data, the fault diagnosis model being obtained by training historical operation data; and determining fault equipment corresponding to the fault result based on the fault result and the digital twin model of the power system.

Description

Technical Field

[0001] The present disclosure relates to the field of park management and digital twin technology, and in particular to a park power monitoring method, device, system, equipment and medium. Background Art

[0002] The digital twin corresponds to the physical world. It builds a model consistent with the physical world in a digital form in the virtual space. Through information interaction with the physical world, it can monitor changes in the physical world and reflect the operating status of the physical world.

[0003] In the existing technology, a large number of digital twin models have been used to monitor the power system in the park. However, the idea of ​​building the existing digital twin model of the park power system is to divide the park into each building, and then start building the digital twin model of the power system at the equipment level. As a result, there are many buildings in the park, the power system structure is huge, and the amount of equipment-level data involved is huge, resulting in excessively high costs. At the same time, there are information islands between buildings and a lack of information linkage, which affects the efficiency of power monitoring. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a park power monitoring method, device, equipment and medium, which effectively solves the technical problems that there are many buildings in the park, the power system structure is huge, the amount of equipment-level data involved is huge, resulting in excessively high costs, and there are information islands between buildings, lack of information linkage, and affect the efficiency of power monitoring.

[0005] In a first aspect, an embodiment of the present disclosure provides a park power monitoring method, the method comprising:

[0006] Obtain the park’s real-time operating data and the park’s corresponding power system digital twin model;

[0007] Input the real-time operation data into the fault diagnosis model to determine the fault result corresponding to the real-time operation data. The fault diagnosis model is trained using historical operation data.

[0008] Based on the fault results and the digital twin model of the power system, the faulty equipment corresponding to the fault results is determined.

[0009] In a possible implementation, in the method provided by the embodiment of the present invention, the fault diagnosis model is trained by the following method:

[0010] Obtaining historical operation data, including the fault results corresponding to the historical operation data;

[0011] Taking historical operation data as input, the fault results corresponding to the output of the historical operation data are compared with the fault results corresponding to the historical operation data, and a fault diagnosis model is generated based on the difference between the fault results corresponding to the output of the historical operation data and the fault results corresponding to the historical operation data.

[0012] In a possible implementation manner, in the method provided in an embodiment of the present invention, obtaining real-time operation data of a park and a digital twin model of a power system corresponding to the park includes:

[0013] Obtain real-time operation data and perform standardized processing on the real-time operation data;

[0014] Determine the digital twin model of the park’s corresponding power system based on the park’s building information data.

[0015] In a possible implementation, in the method provided by the embodiment of the present invention, the method further includes:

[0016] Streamlining the power system digital twin model based on real-time operating data;

[0017] Adjust the park’s equipment based on a streamlined digital twin model of the power system.

[0018] In a possible implementation manner, in the method provided by an embodiment of the present invention, the power system digital twin model includes a park power system digital twin model and a building power system digital twin model, wherein the park power system digital twin model is composed of multiple building power system digital twin models, then after obtaining the real-time operation data of the park and the power system digital twin model corresponding to the park, the method further includes:

[0019] Based on real-time operation data, the digital twin model of the building power system is used to determine the fault prediction results corresponding to the real-time operation data.

[0020] In a possible implementation manner, in the method provided by the embodiment of the present invention, after determining the faulty device corresponding to the fault result based on the fault result and the digital twin model of the power system, the method further includes:

[0021] Compare the fault result and the fault prediction result to obtain a comparison result;

[0022] Modify the digital twin model of the building power system based on the comparison results.

[0023] In a second aspect, an embodiment of the present disclosure provides a park power monitoring device, the device comprising:

[0024] An acquisition unit, used to acquire the real-time operation data of the park and the digital twin model of the power system corresponding to the park;

[0025] A determination unit, used to input the real-time operation data into a fault diagnosis model to determine the fault result corresponding to the real-time operation data, wherein the fault diagnosis model is trained using historical operation data;

[0026] The processing unit is used to determine the faulty device corresponding to the fault result based on the fault result and the digital twin model of the power system.

[0027] In a possible implementation manner, in the device provided by the embodiment of the present invention, the device further includes:

[0028] The model training unit is used to train the fault diagnosis model according to the following method:

[0029] Obtaining historical operation data, where the historical operation data includes fault results corresponding to the historical operation data;

[0030] Taking historical operation data as input, the fault results corresponding to the output of the historical operation data are compared with the fault results corresponding to the historical operation data, and a fault diagnosis model is generated based on the difference between the fault results corresponding to the output of the historical operation data and the fault results corresponding to the historical operation data.

[0031] In a possible implementation manner, in the device provided by the embodiment of the present invention, the acquiring unit is specifically used for:

[0032] Obtain real-time operation data and perform standardized processing on the real-time operation data;

[0033] Determine the digital twin model of the park’s corresponding power system based on the park’s building information data.

[0034] In a possible implementation manner, in the device provided by the embodiment of the present invention, the acquiring unit is further used for:

[0035] Streamlining the power system digital twin model based on real-time operating data;

[0036] Adjust the park’s equipment based on a streamlined digital twin model of the power system.

[0037] In a possible implementation manner, in the device provided by an embodiment of the present invention, the power system digital twin model includes a park power system digital twin model and a building power system digital twin model, wherein the park power system digital twin model is composed of multiple building power system digital twin models, and the determination unit is further used to:

[0038] Based on real-time operation data, the digital twin model of the building power system is used to determine the fault prediction results corresponding to the real-time operation data.

[0039] In a possible implementation manner, in the device provided by the embodiment of the present invention, after determining the faulty device corresponding to the fault result based on the fault result and the digital twin model of the power system, the processing unit is further used to:

[0040] Compare the fault result and the fault prediction result to obtain a comparison result;

[0041] Modify the digital twin model of the building power system based on the comparison results.

[0042] In a third aspect, an embodiment of the present disclosure provides a park power monitoring system, the system comprising a main management center and a plurality of secondary management centers, each of the secondary management centers being communicatively connected to the main management center, wherein:

[0043] The main management center is provided with a fault diagnosis model;

[0044] Each of the secondary management centers is provided with a sensor for acquiring real-time operation data.

[0045] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:

[0046] Memory;

[0047] Processor; and

[0048] Computer programs;

[0049] The computer program is stored in the memory and is configured to be executed by the processor to implement the park power monitoring method as described above.

[0050] In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the park power monitoring method as described above are implemented.

[0051] The present disclosure provides a park power monitoring method, including:

[0052] First, the real-time operation data of the park and the digital twin model of the power system corresponding to the park are obtained, and then the real-time operation data is input into the fault diagnosis model to determine the fault result corresponding to the real-time operation data. The fault diagnosis model is trained using historical operation data. Finally, based on the fault result and the digital twin model of the power system, the faulty device corresponding to the fault result is determined. By applying the park power monitoring method provided by the present disclosure, the power systems of all buildings in the park are uniformly managed through the digital twin model, which strengthens the information exchange between buildings in the park, avoids information islands, and improves management efficiency and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0055] Figure 1 A schematic diagram of a process flow of a park power monitoring method provided by an embodiment of the present disclosure;

[0056] Figure 2 A schematic diagram of the structure of a park power monitoring device provided by an embodiment of the present disclosure;

[0057] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0060] 1. In the embodiments of the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0061] Multidimensional data often contains data from many different fields, and its form, aggregation granularity, etc. all show different characteristics, and the accumulated data scale is very large. In the existing technology, network fault diagnosis and demarcation and positioning require manual observation of various data of different nodes in the time window before and after the fault occurs, which is time-consuming and labor-intensive, and cannot cover all dimensions of data that may cause the fault; in addition, for a given fault type, it is often necessary to rely on manual experience for demarcation, which is very difficult for inexperienced maintenance personnel.

[0062] Figure 1A schematic diagram of a process flow of a park power monitoring method provided by an embodiment of the present disclosure, specifically including the following steps: Figure 1 The following steps S101 to S103 are shown:

[0063] S101. Obtain the real-time operation data of the park and the digital twin model of the power system corresponding to the park.

[0064] During the specific implementation, the model metadata of the streamlined building power system digital twin model of the buildings in all secondary management centers are collected through the main Internet of Things. Specifically, the model metadata of the streamlined building power system digital twin model of the building is first reconstructed to obtain the reconstructed streamlined building power system digital twin model, and then according to the park building drawings, the reconstructed streamlined building power system digital twin model of all buildings is added to the park geographic model to obtain the park power system digital twin model. Finally, the effective real-time operation data of the building power system in all secondary management centers is collected through the main Internet of Things, and the effective real-time operation data is added to the park power system digital twin model.

[0065] S102: Input the real-time operation data into a fault diagnosis model to determine the fault result corresponding to the real-time operation data.

[0066] In the specific implementation, a mechanism fusion data-driven fault diagnosis model is constructed, and the effective real-time operation data of the building power system is input into the mechanism fusion data-driven fault diagnosis model for fault diagnosis reasoning to obtain real-time power system fault diagnosis results. Specifically, firstly, according to the effective historical operation data of the building, the DBN algorithm is used to construct a power system fault diagnosis data sub-model; since the effective historical operation data of the building only includes the key power equipment in the key branches of the power system, the data-driven power system fault diagnosis data sub-model can only learn the relationship between the power system fault and the power system operation data, and cannot accurately locate the power equipment in the power system, for example, the main transformer bus overcurrent and the first bus circuit breaker in the power system, so the power equipment fault reasoning sub-model based on mechanism reasoning is introduced. Then, based on the effective historical operation data of the building, the mechanism data of the power system, and the historical power equipment fault diagnosis results, the ROPN modeling method is used to construct a power equipment fault reasoning sub-model; the fuzzy Petri net modeled by ROPN performs forward and reverse reasoning, reasonably deduces the cause and effect relationship and propagation path of power equipment failure in the power system, outputs the corresponding mechanism analysis of the power equipment failure cause, and locates the power equipment (critical / non-critical power equipment) that has failed in the power system. Then, the power system fault diagnosis data sub-model and the power system fault diagnosis data sub-model are integrated to obtain a mechanism fusion data-driven fault diagnosis model. Finally, the effective real-time operation data of the building power system is input into the mechanism fusion data-driven fault diagnosis model for fault diagnosis reasoning to obtain real-time power system fault diagnosis results, for example, the input line of the main transformer in the power system is short-circuited, resulting in overcurrent;.

[0067] S103: Based on the fault result and the digital twin model of the power system, determine the faulty equipment corresponding to the fault result.

[0068] In specific implementation, the real-time power system fault diagnosis results are added to the digital twin model of the park power system, and the digital twin model of the park power system is used to uniformly manage the power systems of all buildings in the park;

[0069] When repairing power system faults, the main management center sends the power system fault diagnosis results to the corresponding secondary management center through the main IoT gateway. The secondary management center compares and matches the real-time power equipment fault diagnosis results output by the power equipment fault diagnosis model with the power system fault diagnosis results, and uses the building power system digital twin model to locate the exact power equipment location for maintenance.

[0070] Before step S101, the standardized real-time operation data of the power equipment of the building can also be collected in the secondary management center through the Internet of Things, and a digital twin model of the building power system can be constructed based on the standardized real-time operation data and the corresponding building BIM data. The digital twin model of the building power system is used to perform distributed management of the power system of the current building, specifically as follows: first, the real-time operation data of different power equipment of the building is collected through the Internet of Things, and the standardized real-time operation data is standardized to obtain standardized real-time operation data; the standardized processing includes redundant data removal, error data deletion and data normalization, and the normalization formula is:

[0071]

[0072] In the formula, q i_new is the normalized real-time running data; q i is the real-time operation data before normalization; i is the indication value; q max ,q min They are the maximum values ​​of real-time operation data respectively. Then, according to the building BIM data, a three-dimensional static model of the building is constructed, and according to the corresponding building architectural drawings and the three-dimensional scanning video of the building, the three-dimensional static model of the building is corrected to obtain the corrected three-dimensional static model of the building. Then, the static variable data and dynamic variable data in the power equipment BIM data of the building are extracted, and the three-dimensional static model of the power equipment is constructed according to the static variable data, and according to the corresponding power equipment drawings and the three-dimensional scanning video of the power equipment, the three-dimensional static model of the power equipment is corrected to obtain the corrected three-dimensional static model of the power equipment, and then according to the dynamic variable data, the three-dimensional component dynamic model of the power equipment is constructed, and according to the power equipment drawings, the three-dimensional component dynamic model of the power equipment is added to the corresponding corrected three-dimensional static model of the power equipment to obtain the three-dimensional dynamic model of the power equipment. Then, in the secondary IoT gateway of the power equipment, the PCA-XGBoost algorithm is used to build a power equipment fault diagnosis model, and the standardized real-time operation data of the power equipment is input into the power equipment fault diagnosis model to perform power equipment fault diagnosis, and obtain real-time power equipment fault diagnosis results, including the following steps: the power equipment fault diagnosis model uses the PCA principal component analysis method according to the model and function of the current power equipment to obtain k main cost data in the standardized real-time operation data. Specifically, the standardized real-time operation data is converted into an operation data matrix X = [X1, X2, ..., X n ], X n For each row vector of standardized real-time operation data, the transformation matrix P of the operation data matrix X is obtained, and the formula is:

[0073]

[0074] Where D is the covariance matrix of the principal component matrix Y; Y is the principal component matrix; P is the transformation matrix; E is the unit eigenvector matrix;

[0075] Then, according to the running data matrix X and the transformation matrix P, the principal component matrix Y = [Y1, Y2, ..., Y n ],Y n is the nth candidate principal component, and the formula is:

[0076] Y=PX

[0077] Then, according to the cumulative contribution rate of the variance of all candidate principal components, if it exceeds 85%, the corresponding k≤n candidate principal components are used as the principal components. The formula is:

[0078]

[0079] In the formula, λ i is the i-th candidate principal component Y i The variance of; L is the cumulative contribution rate of variance;

[0080] Finally, based on the k main cost data, the standardized real-time operation data is reduced in dimension to obtain the real-time fault prediction data;

[0081] Standardized real-time operation data is acquired by the data acquisition device, including current, voltage, power, temperature, humidity and other data of the power equipment. For different types of power equipment (transformers, generators, mutual inductors or transmission lines), the influence of different data feature quantities is different. The standardized real-time operation data is reduced in dimension through principal component analysis, which reduces the data volume of the standardized real-time operation data, improves the computing efficiency of the secondary IoT gateway, and reduces the data pressure of edge computing and data transmission;

[0082] According to the current real-time fault prediction data of power equipment, the XGBoost algorithm is used to diagnose power equipment faults and obtain the corresponding real-time power equipment fault diagnosis results. The formula is:

[0083]

[0084] In the formula, is the predicted value of the t-th and t-1-th iterations, each of which corresponds to a power equipment fault label, that is, the predicted value is the real-time power equipment fault diagnosis result; q (x i ), f t (x i ) is the tree model of the qth (q≤t) and tth iteration; x i is the i-th real-time fault prediction data;

[0085]

[0086] Where, LOSS is the loss function of the power equipment fault diagnosis model; is the true value y i With the predicted value The loss value; i is the fault prediction data sample indicator; I is the total number of fault prediction data samples;

[0087]

[0088] Where, Obj is the objective function of the power equipment fault diagnosis model; is the sum of the complexity of all tree models;

[0089]

[0090] Where ζ and χ are complexity parameters; T is the number of iterations; is the leaf node weight of the j-th tree model;

[0091] Afterwards, according to the power system topology of the building, the power system topology line is added to the revised three-dimensional static model of the building, the three-dimensional dynamic model of the power equipment is added to the corresponding position of the power system topology line, and the real-time power equipment fault diagnosis result is added to the corresponding three-dimensional dynamic model of the power equipment to obtain the digital twin model of the building power system; in the secondary management center, the digital twin model of the building power system is used to manage the power system of the current building; the digital twin model of the building power system is deployed in the secondary management center of the building to realize the distributed management of each building in the park, and each building can visually manage and detect its own power system; the digital twin model of the building power system The model displays the current building structure, the internal power system and the power equipment used in the power system, and can clearly and understandably display the distribution status of the building power system; a secondary IoT gateway with edge computing capabilities is used to build a power equipment fault diagnosis model, and the real-time power equipment fault diagnosis results are added to the building power system digital twin model, which can perform real-time fault diagnosis on the power equipment in the building power system, providing auxiliary information for real-time management of the building power system; when the power equipment fault diagnosis result is that there is a power equipment fault, the digital twin model of the building power system can be used to accurately locate the corresponding power equipment, perform maintenance and inspections, and improve the management efficiency of the power system.

[0092] The digital twin model of the building power system is simplified to obtain the simplified digital twin model of the building power system, and the standardized real-time operation data of all power equipment is screened according to the simplified digital twin model of the building power system to obtain the effective real-time operation data of the building power system, including the following steps:

[0093] First, according to the building's power system topology and power system mechanism data, the non-critical branches and power equipment in the building power system digital twin model are streamlined to obtain a streamlined building power system digital twin model, which includes the following steps:

[0094] 1. Construct the topological structure expression of the power system, the formula is:

[0095] G=(V,E1(s),E2(s))

[0096] In the formula, G is the expression of the power system topology; V is the set of power equipment nodes; E1 is the bus set; E2 is the branch set; s is the circuit breaker state, E1 (s = 0) / E2 (s = 0), the corresponding bus / branch is disconnected, conversely, E1 (s = 1) / E2 (s = 1), the corresponding bus / branch is connected; G is an undirected graph consisting of edges, points and branch values;

[0097] 2. Obtain the association matrix A = [a ij ], the formula is:

[0098]

[0099] In the formula, a ij is the element of the association matrix A, corresponding to the connectivity with the power equipment node; i and j are both power equipment node indicators, corresponding to V; s is the circuit breaker state of 0 or 1; the association matrix clearly expresses the linkage relationship and control logic relationship of the power system topology structure in a matrix manner;

[0100] 3. According to the power system mechanism data of the building, the non-critical branches and power equipment in the power system topology structure expression are simplified to obtain the simplified power system topology structure expression, the formula is:

[0101] G'=(V',E1(s),E2'(s))

[0102] In the formula, G' is the expression of the simplified power system topology structure; V' is the simplified power equipment node set; E2' is the simplified branch set;

[0103] 4. According to the simplified power system topology expression G', the corresponding elements in the association matrix A are set to zero to obtain the simplified association matrix A' corresponding to the conductive transmission line between the bus and the final load, r(A'-E) = I; where a1 is the row vector of the power equipment node connected to the bus, a I is the row vector of the power equipment nodes of the final load, and the rank of A'-E (unit matrix) = I, which means that there is at least one effective transmission line between the bus and the final load;

[0104] 5. According to the streamlined association matrix, the corresponding power system topology lines and three-dimensional spatial dynamic models of power equipment in the digital twin model of the building power system are deleted to obtain the streamlined digital twin model of the building power system;

[0105] The power system topology includes busbars, power equipment nodes, circuit breakers, and branches formed by transmission lines between busbars and power equipment nodes / circuit breakers, showing the power system distribution of the building. The power system mechanism data includes the operation mechanism of the corresponding busbars and branches in the power system when the circuit breaker is turned on or off, which can reflect the actual operation of the power system and is used to distinguish between critical branches and non-critical branches.

[0106] Through simple matrix operations, the topological structure of the power system can be accurately streamlined, the efficiency and accuracy of the model simplification can be improved, and the normal operation of key branches and key power equipment in the digital twin model of the building power system is guaranteed; then, according to the streamlined digital twin model of the building power system, the standardized real-time operation data of all power equipment are screened to obtain the effective real-time operation data of the building power system. Specifically, first, according to the streamlined association matrix A' corresponding to the streamlined digital twin model of the building power system, the corresponding streamlined power equipment node set V' is extracted; then, according to the streamlined power equipment node set V', all power equipment is screened to obtain effective power equipment; finally, the real-time operation data of the effective power equipment is collected to obtain effective real-time operation data.

[0107] The park power monitoring method provided by the embodiment of the present disclosure can be operated by a park power system digital twin model management system based on the Internet of Things, and the system includes:

[0108] The system includes a main management center and several secondary management centers. All secondary management centers are connected to the main management center through a main IoT gateway, and each secondary management center is connected to all data acquisition devices in the building through a secondary IoT gateway.

[0109] The secondary management center includes a building data server, a power equipment fault diagnosis model construction unit, a building digital twin model construction unit, and a building visualization management unit;

[0110] The building data server is connected to all data acquisition devices in the building through a secondary IoT gateway arranged in the building;

[0111] The data acquisition device is installed at all power equipment in the building power system, including distribution equipment, generators, switch cabinets, transformers, relay protection equipment and power lines. Through sensor technology, it collects real-time operating data of all power equipment and can also transmit its own real-time operating data;

[0112] The secondary IoT gateway is an edge computing gateway with edge computing capabilities. It is used to standardize the real-time operation data of power equipment collected by the data collection device within the current communication range to achieve the purpose of data simplification. The secondary IoT gateway performs fault diagnosis and performs real-time analysis on the standardized real-time operation data of power equipment. A power equipment fault diagnosis model is set up inside. The power equipment fault diagnosis model reduces the data transmission and computing pressure of the secondary management center.

[0113] A power equipment fault diagnosis model building unit, used for training and building according to the historical operation data of the power equipment, and deploying the power equipment fault diagnosis model in the secondary IoT gateway;

[0114] Building data server, used to store standardized real-time operation data, building power equipment BIM data, building architectural drawings, building 3D scanning video, power equipment drawings, power equipment 3D scanning video, real-time power equipment fault diagnosis results, power system topology, building power system mechanism data and building power system digital twin model, and the building data server transmits the standardized real-time operation data (twin data) of each building power system to the main management center in real time through the main Internet of Things gateway;

[0115] The building visualization management unit is used to visualize the digital twin model of the building power system. A building management module is set up in the building visualization management unit to manage the visualized digital twin model of the building power system. The power equipment fault diagnosis model construction unit uses the PCA-XGBoost algorithm for training and construction based on the historical operation data of the power equipment to obtain the power equipment fault diagnosis model and publish it to the corresponding secondary IoT gateway.

[0116] The secondary management center also includes the digital twin model refinement element;

[0117] After the building digital twin model construction unit constructs the building power system digital twin model of the current building in the secondary management center, the building power system digital twin model is simplified using the digital twin model simplification element to obtain the simplified building power system digital twin model, and the standardized real-time operation data of all power equipment is screened based on the simplified building power system digital twin model to obtain the effective real-time operation data of the building power system;

[0118] Collecting data at the system level reduces the pressure of data transmission and processing at the main management center.

[0119] The main management center includes the park data server, the power system fault diagnosis model construction unit, the park digital twin model reconstruction unit, and the park visualization management unit;

[0120] The campus data server is used to communicate with the secondary management center of each building through the main IoT gateway;

[0121] The park digital twin model reconstruction unit is used to reconstruct the park power system digital twin model according to the basic data of each building (including building number and GIS data) and the model metadata of the corresponding streamlined building power system digital twin model, and the park power system digital twin model includes the streamlined building power system digital twin models of all buildings at the corresponding positions in the park;

[0122] A power system fault diagnosis model building unit is used to build a mechanism fusion data driven fault diagnosis model based on effective historical operation data, power system mechanism data and historical power equipment fault diagnosis results;

[0123] The park visualization management unit is used to visualize the digital twin model of the park power system and add the real-time power system fault diagnosis results to the corresponding streamlined building power system digital twin model. The real-time power system fault diagnosis results only serve as a prompt in the main management center. A park management module is set up in the park visualization management unit to uniformly manage the visualized digital twin model of the park power system.

[0124] Figure 2 The schematic diagram of the structure of the park power monitoring device provided by the embodiment of the present disclosure is as follows. The park power monitoring device 200 provided by the embodiment of the present disclosure can execute the processing flow provided by the above park power monitoring method embodiment, such as Figure 2 As shown, the park power monitoring device 200 includes an acquisition unit 201, a determination unit 202 and a processing unit 203, wherein:

[0125] An acquisition unit 201 is used to acquire real-time operation data of the park and a digital twin model of the power system corresponding to the park;

[0126] A determination unit 202 is used to input the real-time operation data into a fault diagnosis model to determine the fault result corresponding to the real-time operation data, and the fault diagnosis model is trained using historical operation data;

[0127] The processing unit 203 is used to determine the faulty device corresponding to the fault result based on the fault result and the digital twin model of the power system.

[0128] In a possible implementation manner, in the device provided by the embodiment of the present invention, the device further includes:

[0129] The model training unit is used to train the fault diagnosis model according to the following method:

[0130] Obtaining historical operation data, including the fault results corresponding to the historical operation data;

[0131] Taking historical operation data as input, the fault results corresponding to the output of the historical operation data are compared with the fault results corresponding to the historical operation data, and a fault diagnosis model is generated based on the difference between the fault results corresponding to the output of the historical operation data and the fault results corresponding to the historical operation data.

[0132] In a possible implementation manner, in the device provided by the embodiment of the present invention, the acquisition unit 201 is specifically used for:

[0133] Obtain real-time operation data and perform standardized processing on the real-time operation data;

[0134] Determine the digital twin model of the park’s corresponding power system based on the park’s building information data.

[0135] In a possible implementation manner, in the device provided by the embodiment of the present invention, the acquiring unit 201 is further used for:

[0136] Streamlining the power system digital twin model based on real-time operating data;

[0137] Adjust the park’s equipment based on a streamlined digital twin model of the power system.

[0138] In a possible implementation manner, in the device provided by an embodiment of the present invention, the power system digital twin model includes a park power system digital twin model and a building power system digital twin model, wherein the park power system digital twin model is composed of multiple building power system digital twin models, and the determination unit 202 is further used to:

[0139] Based on real-time operation data, the digital twin model of the building power system is used to determine the fault prediction results corresponding to the real-time operation data.

[0140] In a possible implementation manner, in the device provided by the embodiment of the present invention, after determining the faulty device corresponding to the fault result based on the fault result and the digital twin model of the power system, the processing unit 203 is further used to:

[0141] Compare the fault result and the fault prediction result to obtain a comparison result;

[0142] Modify the digital twin model of the building power system based on the comparison results.

[0143] Figure 2 The park power monitoring device of the illustrated embodiment can be used to execute the technical solution of the above-mentioned method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.

[0144] In addition, combined Figure 1-Figure 2 The campus power monitoring method and device described in the embodiments of the present application can be implemented by electronic equipment. Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0145] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303 to implement the park power monitoring method of the embodiment described in the present disclosure. In RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0146] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0147] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart, thereby implementing the voice control method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0148] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0149] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0150] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0151] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0152] Obtain the park’s real-time operating data and the park’s corresponding power system digital twin model;

[0153] Input the real-time operation data into the fault diagnosis model to determine the fault result corresponding to the real-time operation data. The fault diagnosis model is trained using historical operation data.

[0154] Based on the fault results and the digital twin model of the power system, the faulty equipment corresponding to the fault results is determined.

[0155] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0156] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0157] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0159] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0160] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0161] The present disclosure provides a park power monitoring method, including:

[0162] First, the real-time operation data of the park and the digital twin model of the power system corresponding to the park are obtained, and then the real-time operation data is input into the fault diagnosis model to determine the fault result corresponding to the real-time operation data. The fault diagnosis model is trained using historical operation data. Finally, based on the fault result and the digital twin model of the power system, the faulty device corresponding to the fault result is determined. By applying the park power monitoring method provided by the present disclosure, the power systems of all buildings in the park are uniformly managed through the digital twin model, which strengthens the information exchange between buildings in the park, avoids information islands, and improves management efficiency and effectiveness.

[0163] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0164] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0167] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A park power monitoring method, characterized in that: The method comprises: Acquire real-time operation data of the park and a digital twin model of the power system corresponding to the park; Inputting the real-time operation data into a fault diagnosis model to determine a fault result corresponding to the real-time operation data, wherein the fault diagnosis model is trained using historical operation data; Based on the fault result and the power system digital twin model, a faulty device corresponding to the fault result is determined.

2. The method according to claim 1, characterized in that The fault diagnosis model is trained by the following method: Acquire the historical operation data, wherein the historical operation data includes a fault result corresponding to the historical operation data; Taking the historical operation data as input, the fault result corresponding to the output of the historical operation data is compared with the fault result corresponding to the historical operation data, and the fault diagnosis model is generated by training based on the difference between the fault result corresponding to the output of the historical operation data and the fault result corresponding to the historical operation data.

3. The method according to claim 2, characterized in that The obtaining of the real-time operation data of the park and the digital twin model of the power system corresponding to the park includes: Acquiring the real-time operation data and performing standardization processing on the real-time operation data; A digital twin model of the power system corresponding to the park is determined based on the building information data of the park.

4. The method according to claim 3, characterized in that: The method further comprises: Simplifying the power system digital twin model according to the real-time operation data; The equipment in the park is adjusted based on the streamlined digital twin model of the power system.

5. The method according to claim 4, characterized in that The power system digital twin model includes a park power system digital twin model and a building power system digital twin model, wherein the park power system digital twin model is composed of a plurality of building power system digital twin models, then after acquiring the real-time operation data of the park and the power system digital twin model corresponding to the park, the method further includes: Based on the real-time operation data, the digital twin model of the building power system is used to determine the fault prediction result corresponding to the real-time operation data.

6. The method according to claim 5, characterized in that After determining the faulty device corresponding to the fault result based on the fault result and the power system digital twin model, the method further includes: Comparing the fault result with the fault prediction result to obtain a comparison result; The digital twin model of the building power system is modified according to the comparison result.

7. A park power monitoring device, characterized in that: The device comprises: An acquisition unit, used to acquire real-time operation data of the park and a digital twin model of the power system corresponding to the park; a determination unit, configured to input the real-time operation data into a fault diagnosis model to determine a fault result corresponding to the real-time operation data, wherein the fault diagnosis model is trained using historical operation data; A processing unit is used to determine a faulty device corresponding to the fault result based on the fault result and the digital twin model of the power system.

8. A park power monitoring system, characterized in that: The system includes a main management center and a plurality of secondary management centers, each of which is in communication connection with the main management center, wherein: The main management center is provided with a fault diagnosis model; Each of the secondary management centers is provided with a sensor for acquiring real-time operation data.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the park power monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the park power monitoring method according to any one of claims 1 to 7 is implemented.

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