Site selection method and device for 5G base station in transformer substation and computer equipment

Abstract

The invention relates to a site selection method and device for a 5G base station in a transformer substation, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring historical data of power equipment at each position point in a transformer substation; the historical data comprises at least one of the following data: operation data, environment data and fault data; selecting feature data from the historical data; the feature data comprises data having an association relationship with a fault of the power equipment; inputting the feature data into a preset fault prediction model to obtain a fault parameter of each position point; the fault parameters comprise at least one of the following: a fault occurrence probability and a fault type; determining candidate position points from the position points based on the fault parameters, and acquiring historical electromagnetic field data of the candidate position points; and determining a target position point according to the historical electromagnetic field data of each candidate position point. By adopting the method, the operation stability and reliability of the 5G base station can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, storage medium and computer program product for site selection of a 5G base station in a substation. Background Art

[0002] With the advancement of communications technology, the penetration of 5G networks is accelerating. An increasing number of 5G base stations are being deployed on power infrastructure, such as power towers and substations, to facilitate the rapid construction and widespread coverage of 5G networks. However, the complex electromagnetic environment within substations can negatively impact the normal operation of 5G base stations and even damage circuit components within them, impacting the quality of network service. Traditionally, base station site selection relies on simple geographic information and environmental data assessments, primarily based on empirical evidence, to determine the installation location of 5G base stations. However, this approach lacks systematic data support and comprehensive risk assessments, resulting in potentially unscientific base station site selection decisions. Consequently, the selected locations may experience higher failure rates in actual operation, impacting network stability and user experience. Summary of the Invention

[0003] Based on this, it is necessary to provide a site selection method, device, computer equipment, computer-readable storage medium and computer program product for 5G base stations in substations to address the above technical problems.

[0004] In a first aspect, the present application provides a method for selecting a site for a 5G base station in a substation. The method comprises:

[0005] Acquire historical data of power equipment at various locations within the substation; wherein the historical data includes at least one of the following: operating data, environmental data, and fault data;

[0006] Selecting characteristic data from each of the historical data; wherein the characteristic data includes data associated with a fault of the power equipment;

[0007] Inputting the characteristic data into a preset fault prediction model to obtain fault parameters of each of the location points; wherein the fault parameters include at least one of the following: probability of fault occurrence and fault type;

[0008] Determine candidate location points from the location points based on the fault parameters, and obtain historical electromagnetic field data of each candidate location point;

[0009] The target location point is determined based on the historical electromagnetic field data of each candidate location point.

[0010] In one embodiment, the method for obtaining the fault prediction model includes:

[0011] Obtain historical operating data, historical fault types, and corresponding fault data for each location point;

[0012] Identify the failure cycle of each node at each location based on historical failure types and corresponding failure data, and use the failure cycle and failure type to establish an initial failure prediction model;

[0013] Establish sample data based on historical operation data, historical fault types and corresponding fault data; wherein the sample data includes training set data and test set data;

[0014] The initial fault prediction model is trained using the training set data, and the parameters of the trained initial fault prediction model are adjusted using the test set data to obtain a fault prediction model.

[0015] In one embodiment, the method further comprises:

[0016] Establishing a 5G base station at a target location of the substation; wherein the substation includes a primary communication link and a backup communication link;

[0017] In the event of a failure of the main communication link of the substation, communication is performed using a backup communication link.

[0018] In one embodiment, the substation further includes dedicated network resources, and in the event of a failure of a primary communication link in the substation, communication is performed using a backup communication link, including:

[0019] When a main communication link of the substation fails, obtaining a service type of each communication service of the substation;

[0020] When the service type is a target service, switching the communication service to a dedicated network resource for communication;

[0021] In the case that the service type is not the target service, communication is performed using the backup communication link.

[0022] In one embodiment, the method further comprises:

[0023] Obtain the operating frequency of the 5G base station and the operating frequency of the preset communication equipment in the substation;

[0024] When the operating frequency of the 5G base station conflicts with the operating frequency of the communication device, the operating frequency of the 5G base station is adjusted.

[0025] In one embodiment, the method further comprises:

[0026] Regularly obtain electromagnetic environment data of the 5G base station;

[0027] When the electromagnetic environment data reaches a target threshold, an early warning signal is generated.

[0028] In a second aspect, the present application also provides a device for selecting a site for a 5G base station in a substation. The device includes:

[0029] A data acquisition module is used to acquire historical data of power equipment at various locations within the substation; wherein the historical data includes at least one of the following: operating data, environmental data, and fault data;

[0030] A data selection module, configured to select characteristic data from each of the historical data; wherein the characteristic data includes data associated with a fault of the power equipment;

[0031] A parameter prediction module, configured to input the characteristic data into a preset fault prediction model to obtain fault parameters for each of the location points; wherein the fault parameters include at least one of the following: probability of fault occurrence and fault type;

[0032] The data acquisition module is further configured to determine a candidate location point from the location points based on the fault parameters, and acquire historical electromagnetic field data of each candidate location point;

[0033] The location determination module is used to determine the target location point based on the historical electromagnetic field data of each candidate location point.

[0034] In one embodiment, the apparatus further includes a model training module for:

[0035] Obtain historical operating data, historical fault types, and corresponding fault data for each location point;

[0036] Identify the failure cycle of each node at each location based on historical failure types and corresponding failure data, and use the failure cycle and failure type to establish an initial failure prediction model;

[0037] Establish sample data based on historical operation data, historical fault types and corresponding fault data; wherein the sample data includes training set data and test set data;

[0038] The initial fault prediction model is trained using the training set data, and the parameters of the trained initial fault prediction model are adjusted using the test set data to obtain a fault prediction model.

[0039] In one embodiment, the apparatus further comprises:

[0040] A base station establishment module, configured to establish a 5G base station at a target location of the substation; wherein the substation includes a primary communication link and a backup communication link;

[0041] The backup communication module is used to communicate using the backup communication link when a main communication link of the substation fails.

[0042] In one embodiment, the substation further includes dedicated network resources, and the backup communication module includes:

[0043] A service acquisition submodule, configured to acquire the service type of each communication service of the substation when a failure occurs in the main communication link of the substation;

[0044] A dedicated communication submodule, configured to switch the communication service to a dedicated network resource for communication when the service type is a target service;

[0045] The backup communication submodule is used to communicate using the backup communication link when the service type is not the target service.

[0046] In one embodiment, the apparatus further comprises:

[0047] Frequency acquisition module, used to obtain the operating frequency of the 5G base station and the operating frequency of the preset communication equipment in the substation;

[0048] A frequency adjustment module is used to adjust the operating frequency of the 5G base station when the operating frequency of the 5G base station conflicts with the operating frequency of the communication device.

[0049] In one embodiment, the apparatus further comprises:

[0050] The data acquisition module is further configured to periodically acquire electromagnetic environment data of the 5G base station;

[0051] The signal generation module is used to generate an early warning signal when the electromagnetic environment data reaches a target threshold.

[0052] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the site selection method for a 5G base station in a substation as described in any one of the embodiments of the present disclosure.

[0053] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for selecting a site for a 5G base station in a substation as described in any one of the embodiments of the present disclosure.

[0054] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, which, when executed by a processor, implements the method for selecting a site for a 5G base station in a substation as described in any one of the embodiments of the present disclosure.

[0055] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for site selection of 5G base stations within substations obtains historical data from power equipment at each location, selects characteristic data based on this data, and inputs it into a preset fault prediction model to obtain fault parameters for each location. Target locations are then determined based on these parameters and historical electromagnetic field data. This method not only considers geographic information and environmental data but also incorporates the actual operation of power equipment and fault data, enabling a more accurate assessment of the fault risk and electromagnetic environment at each location. Furthermore, determining target locations based on fault parameters and historical electromagnetic field data comprehensively considers factors such as electromagnetic compatibility, equipment layout, and ease of maintenance, ensuring the scientific and rationality of 5G base station site selection. This site selection method not only improves the stability and reliability of 5G base station operation but also reduces failure rates, thereby enhancing network stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 1 is a flow chart of a method for selecting a site for a 5G base station in a substation according to an embodiment;

[0057] Figure 2 A schematic diagram of a process for obtaining a fault prediction model in one embodiment;

[0058] Figure 3 FIG1 is a schematic diagram of a process flow of standby link communication in one embodiment;

[0059] Figure 4 1 is a flow chart of dedicated link communication in one embodiment;

[0060] Figure 5 1. A flow chart of adjusting the communication operating frequency in one embodiment;

[0061] Figure 6 A schematic diagram of a process for generating a warning signal in one embodiment;

[0062] Figure 7 This is a structural block diagram of a site selection device for a 5G base station in a substation in one embodiment;

[0063] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0065] In one embodiment, Figure 1 As shown, a method for selecting a site for a 5G base station in a substation is provided. This embodiment uses the method applied to a terminal as an example. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0066] Step S100: Acquire historical data of power equipment at various locations within a substation; wherein the historical data includes at least one of the following: operating data, environmental data, and fault data.

[0067] In an exemplary embodiment, the substation may have multiple locations, etc., and one of the locations may be selected for installing a 5G base station, etc.

[0068] In an exemplary embodiment, the operating data may include the operating status of the power equipment, such as whether it is running, current, voltage, power, etc.; the environmental data may include temperature, humidity, etc.; the fault data may include historical equipment load, historical voltage fluctuations, historical current mutations and other data.

[0069] In an exemplary embodiment, the substation may include a location that converts voltage and current in the power system, receives electrical energy, and distributes it. It is understood that 5G base stations generally require uninterrupted power support and specific low-voltage power to support the normal operation of their equipment. Therefore, 5G base stations can be deployed in substations to convert high-voltage power in the power system into low-voltage power suitable for 5G base station equipment and ensure stable power distribution. It is understood that each substation may include multiple different power equipment for different power control functions.

[0070] In an exemplary embodiment, the power equipment may include devices for receiving, converting, and distributing electrical energy. Specifically, the power receiving equipment may include transformers, switchgear, etc.; the power conversion equipment may include rectifiers, inverters, etc.; and the power distribution equipment may include distribution panels, circuit breakers, etc. In actual use, the power substation's power equipment may include transformers, switchgear, capacitor banks, lightning arresters, and cables. The operating status and performance of these power equipment directly affect the power supply quality and stability of 5G base stations and substations. For example, transformers are responsible for converting high-voltage electrical energy into low-voltage electrical energy suitable for 5G base stations; switchgear is used to control and protect circuits, ensuring that power can be quickly cut off in the event of a power failure to prevent equipment damage; capacitor banks are used to compensate for the reactive power of the power system and improve the power factor; lightning arresters are used to protect power equipment from lightning overvoltage damage; and cables are responsible for transmitting electrical energy to various electrical devices.

[0071] In an exemplary embodiment, the historical data may be collected in real time by sensors installed in the substation and stored in a database for subsequent analysis and use.

[0072] Step S200 , selecting characteristic data from each of the historical data; wherein the characteristic data includes data associated with a fault of the power equipment.

[0073] In an exemplary embodiment, the characteristic data may include equipment load, voltage fluctuation, current mutation, etc. The characteristic data may also include current, voltage, power, etc., and voltage fluctuation, etc. may be determined by voltage.

[0074] In an exemplary embodiment, by analyzing and processing historical data and utilizing machine learning algorithms or data mining techniques, characteristic data associated with power equipment failures can be extracted from a large amount of historical data for subsequent failure prediction.

[0075] In an exemplary embodiment, the fault may include an electromagnetic transient event, and may also include the intensity of the electromagnetic field causing the 5G base station network signal to continue to drop, etc.

[0076] Step S300: input the characteristic data into a preset fault prediction model to obtain fault parameters of each of the location points; wherein the fault parameters include at least one of the following: probability of fault occurrence and fault type.

[0077] In one exemplary embodiment, the fault prediction model can be built based on a machine learning algorithm or a deep learning algorithm. By training and learning from historical data, it can predict the probability and type of faults occurring at various locations. The fault prediction model can select appropriate algorithms and parameters based on actual needs to improve prediction accuracy and reliability.

[0078] In one exemplary embodiment, the fault types may include electromagnetic transient faults, overload faults, short circuit faults, etc. The fault prediction model can be used to predict and analyze different types of faults, providing more comprehensive data support for subsequent site selection decisions. In another exemplary embodiment, the fault impact range can be determined based on the fault type.

[0079] Step S400 : determining candidate location points from the location points based on the fault parameters, and acquiring historical electromagnetic field data of the candidate location points.

[0080] In an exemplary embodiment, a location point with a low probability of failure and a small impact range of the fault type may be selected as a candidate location point based on the fault parameters.

[0081] In one exemplary embodiment, the historical electromagnetic field data can include electromagnetic field strength, electromagnetic field frequency, and other information. This data can be acquired using specialized electromagnetic field measurement equipment to comprehensively assess the electromagnetic environment at candidate locations. After acquiring the historical electromagnetic field data for candidate locations, this data can be further analyzed to determine which locations are most suitable for installing 5G base stations, thereby ensuring the proper operation of the base stations and network stability.

[0082] Step S500: determining a target location point based on historical electromagnetic field data of each candidate location point.

[0083] In an exemplary embodiment, the candidate location point with the smallest historical electromagnetic field data may be selected as the target location point.

[0084] In one exemplary embodiment, a comprehensive assessment of candidate locations, including their electromagnetic compatibility, equipment layout, and ease of maintenance, can be performed to determine the optimal target location. Once the target location is determined, the electromagnetic environment at that location can be monitored in real time to ensure the stability and reliability of the 5G base station during actual operation. Furthermore, the device can include a module for monitoring and analyzing the operating status of the 5G base station to promptly identify and address potential faults, further improving network stability.

[0085] In the aforementioned method for selecting a 5G base station site within a substation, historical data on power equipment at each location is obtained. Feature data is selected based on this data and input into a preset fault prediction model to obtain fault parameters for each location. The target location is then determined based on the fault parameters and historical electromagnetic field data for each location. This method not only considers geographic information and environmental data but also incorporates the actual operating conditions and fault data of the power equipment, enabling a more accurate assessment of the fault risk and electromagnetic environment at each location. Furthermore, the target location is determined based on fault parameters and historical electromagnetic field data, taking into account factors such as electromagnetic compatibility, equipment layout, and ease of maintenance, ensuring the scientific and rational nature of the 5G base station site selection. This site selection method not only improves the stability and reliability of 5G base station operation but also reduces the failure rate, thereby enhancing network stability.

[0086] In one embodiment, Figure 2 As shown, the method for obtaining the fault prediction model includes:

[0087] Step S301: Acquire historical operation data, historical fault types, and corresponding fault data of each location point.

[0088] Step S302 : identifying the failure cycle of each location node according to the historical failure type and the corresponding failure data, and establishing an initial failure prediction model using the failure cycle and failure type.

[0089] In an exemplary embodiment, a statistical analysis method (such as autocorrelation function ACF and partial autocorrelation function PACF) may be used to identify historical fault types and corresponding fault data, thereby identifying trends and periodicity in time series data.

[0090] In an exemplary embodiment, the initial fault prediction model may adopt a deep learning model (such as an LSTM network) or a traditional statistical model (such as an ARIMA model).

[0091] In an exemplary embodiment, the historical fault types and corresponding fault data may include which faults have occurred in the past, and which fault data appeared when such faults occurred.

[0092] Step S303: creating sample data based on historical operation data, historical fault types, and corresponding fault data; wherein the sample data includes training set data and test set data.

[0093] Step S304: using the training set data to train the initial fault prediction model, and using the test set data to adjust the parameters of the trained initial fault prediction model to obtain a fault prediction model.

[0094] In one exemplary embodiment, the model can be trained using a training set to learn fault patterns and patterns, and a test set can be used to determine the model's accuracy and adjust the model's parameters to improve the model's prediction accuracy. In another exemplary embodiment, the model's prediction performance can be evaluated using methods such as cross-validation to ensure the model's accuracy and generalization ability.

[0095] In one exemplary embodiment, the fault prediction model can also be used for monitoring and prediction. Specifically, the trained model is deployed into the substation's monitoring system to enable real-time monitoring of the operating status of power equipment. The model can then be used to analyze real-time data and predict potential electromagnetic transient events.

[0096] In one exemplary embodiment, the model can also include corresponding response strategies. When the model predicts a possible electromagnetic transient event, it can issue a timely warning and provide corresponding response strategies. Based on the prediction results, the operating parameters of the power equipment can be adjusted to avoid or mitigate the impact of the electromagnetic transient event.

[0097] In one exemplary embodiment, the fault prediction model can also continuously collect new operational data over time to update the model's training dataset. The model is regularly retrained and optimized to adapt to changes in the power system and emerging electromagnetic transient patterns. Through these steps, machine learning algorithms can help substations effectively predict and prevent electromagnetic transient events, improving the stability and reliability of the power system.

[0098] In this embodiment, by constructing a fault prediction model and using it to predict and analyze the fault risks at various locations within the substation, the likelihood and type of faults at each location can be more accurately assessed. This data-driven fault prediction method is more scientific and accurate than traditional, experience-based site selection methods. Furthermore, a comprehensive assessment of candidate locations, combined with historical electromagnetic field data, ensures the rationality of 5G base station site selection and reduces the risk of base station failures due to electromagnetic environment issues. By monitoring and analyzing the operating status of 5G base stations in real time, potential faults can be promptly identified and addressed, further improving network stability and reliability.

[0099] In one embodiment, Figure 3 As shown, the method further includes:

[0100] Step S601: Establish a 5G base station at the target location of the substation; wherein the substation includes a main communication link and a backup communication link.

[0101] In one exemplary embodiment, after a 5G base station is established, an intelligent monitoring system can be used to monitor the electromagnetic environment and base station operating status in real time. Upon detecting an electromagnetic transient event, the system automatically triggers pre-defined response measures. Based on the nature and severity of the monitored electromagnetic transient event, the intelligent system can also automatically adjust the base station's transmit power to reduce or eliminate electromagnetic interference. This can be achieved through the base station's remote management system, which can dynamically adjust power levels based on environmental changes.

[0102] In one exemplary embodiment, directional antenna technology can be used to align the electromagnetic beam of a 5G base station directly at the area requiring coverage, reducing electromagnetic interference with substation equipment. In another exemplary embodiment, low-power 5G base station equipment can be used to reduce electromagnetic radiation intensity, thereby lowering the risk of electromagnetic transient events. In another exemplary embodiment, a specific 5G base station installation area can be designated within the substation, maintaining a safe distance from high-voltage equipment to reduce electromagnetic interference, among other things.

[0103] Step S602: When a main communication link of the substation fails, communication is performed using a backup communication link.

[0104] In one exemplary embodiment, multiple backup communication links can be preset in the 5G base station's communication system. When the primary link is affected by an electromagnetic transient event, the system can automatically switch to the backup link to ensure communication continuity and reliability. This process can be implemented through Xn handover signaling, which involves steps such as establishing an Xn link, preparing for handover, and executing the handover.

[0105] In one exemplary embodiment, after establishing 5G base stations, an intelligent monitoring system can also be established. Leveraging the high-speed data transmission capabilities of the 5G network, this system can be deployed to monitor the status of power grid equipment in real time and quickly identify and respond to electromagnetic transient events. An automated control system can also be integrated to immediately initiate pre-defined mitigation measures upon detecting an electromagnetic transient event, such as adjusting base station power or switching to a backup communication link. Simultaneously, machine learning algorithms can be used to analyze historical data to predict and prevent potential electromagnetic transient events.

[0106] In this embodiment, by establishing a 5G base station at the target location of the substation and configuring the main communication link and the backup communication link, the redundancy and reliability of the communication system are enhanced. When the main communication link is interrupted due to an electromagnetic transient event or other fault, the backup communication link can quickly take over the communication task to ensure that the data transmission between the substation and the remote control center is not affected. In addition, the application of the intelligent monitoring system makes the monitoring of the electromagnetic environment and the operating status of the base station more accurate and real-time. By intelligently adjusting the transmission power of the base station and automatically switching the communication link, the system can effectively respond to electromagnetic transient events and reduce their impact on the operation of the 5G base station and the stability of the communication network. This comprehensive application of data-driven site selection, fault prediction model, intelligent monitoring system and redundant communication link solution has significantly improved the scientificity and rationality of the site selection of 5G base stations in substations, as well as the stability and reliability of the operation of 5G base stations, providing strong support for the intelligence and network security of the power system.

[0107] In one embodiment, Figure 4 As shown, the substation further includes dedicated network resources, and in the event of a failure of the main communication link of the substation, communication is performed using a backup communication link, including:

[0108] Step S611: When a failure occurs in a main communication link of the substation, the service type of each communication service of the substation is obtained.

[0109] Step S612: When the service type is a target service, the communication service is switched to a dedicated network resource for communication.

[0110] Step S613: When the service type is not the target service, communication is performed using the backup communication link.

[0111] In one exemplary embodiment, the 5G base station can utilize network slicing technology within the 5G network to allocate dedicated network resources for critical communications services within the substation. In the event of an electromagnetic transient, these dedicated resources can be quickly switched to, ensuring that critical services remain unaffected. In actual use, DAPS within a 5G network allows user devices to maintain simultaneous connections to both the source and target cells during handover. This ensures communication continuity and minimizes interruption time even during electromagnetic transient events.

[0112] In one exemplary embodiment, regular electromagnetic radiation environmental monitoring can be conducted to ensure that base station electromagnetic radiation levels comply with national standards. This helps prevent and reduce the occurrence of electromagnetic transient events. In another exemplary embodiment, in the event that an electromagnetic transient event could cause communication disruption, an emergency communication solution can be prepared, such as deploying 5G emergency communication vehicles to provide temporary network coverage and communication capabilities.

[0113] In this embodiment, by providing dedicated network resources for the key communication services of the substation and intelligently selecting the communication method according to the service type when the main communication link fails, the reliability and flexibility of communication are further improved. The provision of dedicated network resources ensures the communication continuity of key services in extreme situations such as electromagnetic transient events, and reduces the potential risks caused by communication interruptions. At the same time, regular electromagnetic radiation environment monitoring and the preparation of emergency communication plans also provide a strong guarantee for the safe operation of 5G base stations. This solution that comprehensively applies multiple technical means not only improves the rationality of 5G base station site selection in substations, but also significantly enhances the stability and reliability of 5G base station operation, providing solid support for the intelligence and network security of the power system.

[0114] In one embodiment, Figure 5 As shown, the method further includes:

[0115] Step S621, obtain the operating frequency of the 5G base station and the operating frequency of the preset communication equipment in the substation.

[0116] Step S622: When the operating frequency of the 5G base station conflicts with the operating frequency of the communication device, adjust the operating frequency of the 5G base station.

[0117] In one exemplary embodiment, a frequency management system can monitor the operating frequencies of 5G base stations and other communication equipment within substations in real time. Once a frequency conflict is detected, the system automatically adjusts the operating frequency of the 5G base station to avoid interference. This can be achieved by adjusting the base station's transmission frequency or selecting other available frequency bands. In another exemplary embodiment, a frequency conflict handling strategy can be pre-set. When a frequency conflict is detected, the system automatically performs corresponding adjustments based on the preset strategy to improve processing efficiency and accuracy. In addition, regular frequency planning and optimization of communication equipment within 5G base stations and substations is also an important measure to ensure smooth communication and reduce frequency conflicts.

[0118] In one exemplary embodiment, a dynamic frequency management strategy can be implemented to ensure that the operating frequency of 5G base stations is coordinated with the frequencies of other communication equipment within the substation to avoid frequency conflicts. In another exemplary embodiment, 5G base stations can be made frequency agile, automatically adjusting their operating frequency based on real-time electromagnetic environment changes to reduce interference. In another exemplary embodiment, 5G network slicing technology can be used to allocate dedicated network resources to critical communication services in the substation, ensuring communication stability in environments with electromagnetic interference.

[0119] In this embodiment, the stability and reliability of the communication system are significantly improved by real-time monitoring of the operating frequencies of 5G base stations and other communication equipment within the substation, and automatic adjustment of the 5G base station's operating frequency to avoid frequency conflicts. This dynamic frequency management strategy not only reduces the occurrence of frequency conflicts but also ensures smooth coordination between the 5G base station and other communication equipment within the substation. Furthermore, the frequency agility of the 5G base station and the application of network slicing technology further enhance the stability and anti-interference capabilities of the communication system in electromagnetic interference environments. This solution, which integrates multiple technical means, provides more solid support for the intelligentization and network security of the power system.

[0120] In one embodiment, Figure 6 As shown, the method further includes:

[0121] Step S631: Regularly obtain the electromagnetic environment data of the 5G base station.

[0122] Step S632: When the electromagnetic environment data reaches a target threshold, a warning signal is generated.

[0123] In one exemplary embodiment, an intelligent monitoring system can monitor the electromagnetic environment data around 5G base stations in real time, including electric and magnetic field strengths. Once this data reaches a preset target threshold, the system immediately generates a warning signal, prompting relevant personnel to take necessary measures. This helps to promptly detect potential electromagnetic interference or excessive electromagnetic radiation, ensuring the safe operation of the 5G base station and the electromagnetic compatibility of the surrounding environment. In another exemplary embodiment, the warning signal can trigger an automatic adjustment mechanism, such as adjusting the 5G base station's transmit power or switching the communication frequency band to reduce electromagnetic interference or radiation levels. In this way, intelligent management and control of the 5G base station's electromagnetic environment can be achieved, further improving the stability and security of the communication system.

[0124] In one exemplary embodiment, a detailed electromagnetic compatibility analysis can be conducted before 5G base station deployment to assess potential electromagnetic interference and sensitivity. In another exemplary embodiment, a real-time electromagnetic field strength monitoring system can be deployed to continuously monitor the electromagnetic environment within the substation to ensure that the operation of the 5G base station does not cause electromagnetic transient issues. In another exemplary embodiment, shielding and effective grounding measures can be implemented for 5G base station equipment to reduce electromagnetic leakage and induced currents.

[0125] In this embodiment, by regularly monitoring the electromagnetic environment data of 5G base stations and generating early warning signals when the data reaches the target threshold, a strong guarantee is provided for the safe operation of 5G base stations. This intelligent electromagnetic environment management strategy not only helps to promptly detect potential electromagnetic interference problems, but also triggers an automatic adjustment mechanism when necessary to reduce electromagnetic interference or radiation levels, ensuring the stability and security of the communication system. At the same time, detailed electromagnetic compatibility analysis, the deployment of a real-time electromagnetic field strength monitoring system, and the shielding and grounding measures of 5G base station equipment also provide comprehensive support for the safe operation and electromagnetic compatibility of 5G base stations. This solution, which comprehensively applies multiple technical means, not only improves the rationality of 5G base station site selection and operational stability, but also provides solid technical support for the intelligentization and network security of the power system.

[0126] In an exemplary embodiment, utilizing network slicing technology may also include demand analysis: specifically, a detailed analysis of the requirements for critical substation communication services, including specific performance requirements for bandwidth, latency, reliability, and number of connections. These services may include intelligent inspection, distribution network differential protection, and metering automation. Network slicing design may also include designing differentiated 5G network slices based on service requirements. This includes both hard slicing and soft slicing solutions. Hard slicing provides end-to-end configuration of dedicated resources and hard-isolated pipes, while soft slicing provides a degree of logical isolation and reuses operator resources. Core network configuration may also include configuring dedicated network slices within the 5G core network for the substation's critical services. This may involve configuration of the control and user planes, including the setup of network elements such as the AMF, SMF, NSSF, and UDM. Bearer network optimization may also include using FlexE technology to achieve physical isolation and bandwidth guarantees on the bearer network, establishing end-to-end deterministic SLA service paths. It also includes wireless network isolation: Dedicated wireless resources are provided on the wireless network for the substation's critical services through methods such as RB resource reservation, ensuring isolation from other services. It also includes security isolation: Physical isolation of services between the production and management areas, as well as logical isolation between services within the production and management areas, to meet high-security isolation requirements. It also includes operational management capabilities: Leveraging the capability exposure and operational management capabilities of 5G slicing networks, grid services are visualized, manageable, and controllable. This includes rapid provisioning of slices or new services, real-time monitoring of network resources, and fault location. It also includes key technology applications: Utilizing 5G LAN technology to support the Layer 2 multicast communication requirements of services such as differential protection for power distribution networks. Furthermore, edge-to-edge collaboration ensures low latency and high reliability. It also includes performance verification: Through actual testing, network slice provisioning, PRB reservation, communication latency, and inter-slice interference are verified to ensure they meet the requirements of critical substation services. It also includes continuous optimization: Based on test results and service development, network slice configuration is continuously optimized to adapt to changes and developments in critical substation communication services.

[0127] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0128] Based on the same inventive concept, the embodiments of the present application also provide a device for selecting a site for a 5G base station in a substation, which is used to implement the aforementioned method for selecting a site for a 5G base station in a substation. The implementation solution provided by the device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of the device for selecting a site for a 5G base station in a substation provided below can be found in the above-mentioned limitations on the method for selecting a site for a 5G base station in a substation, and will not be repeated here.

[0129] In one embodiment, Figure 7 As shown, a site selection device 100 for a 5G base station in a substation is provided, comprising: a data acquisition module 101, a data selection module 102, a parameter prediction module 103 and a location determination module 104, wherein:

[0130] A data acquisition module is used to acquire historical data of power equipment at various locations within the substation; wherein the historical data includes at least one of the following: operating data, environmental data, and fault data;

[0131] A data selection module, configured to select characteristic data from each of the historical data; wherein the characteristic data includes data associated with a fault of the power equipment;

[0132] A parameter prediction module, configured to input the characteristic data into a preset fault prediction model to obtain fault parameters for each of the location points; wherein the fault parameters include at least one of the following: probability of fault occurrence and fault type;

[0133] The data acquisition module is further configured to determine a candidate location point from the location points based on the fault parameters, and acquire historical electromagnetic field data of each candidate location point;

[0134] The location determination module is used to determine the target location point based on the historical electromagnetic field data of each candidate location point.

[0135] In one embodiment, the apparatus further includes a model training module for:

[0136] Obtain historical operating data, historical fault types, and corresponding fault data for each location point;

[0137] Identify the failure cycle of each node at each location based on historical failure types and corresponding failure data, and use the failure cycle and failure type to establish an initial failure prediction model;

[0138] Establish sample data based on historical operation data, historical fault types and corresponding fault data; wherein the sample data includes training set data and test set data;

[0139] The initial fault prediction model is trained using the training set data, and the parameters of the trained initial fault prediction model are adjusted using the test set data to obtain a fault prediction model.

[0140] In one embodiment, the apparatus further comprises:

[0141] A base station establishment module, configured to establish a 5G base station at a target location of the substation; wherein the substation includes a primary communication link and a backup communication link;

[0142] The backup communication module is used to communicate using the backup communication link when a main communication link of the substation fails.

[0143] In one embodiment, the substation further includes dedicated network resources, and the backup communication module includes:

[0144] A service acquisition submodule, configured to acquire the service type of each communication service of the substation when a failure occurs in the main communication link of the substation;

[0145] A dedicated communication submodule, configured to switch the communication service to a dedicated network resource for communication when the service type is a target service;

[0146] The backup communication submodule is used to communicate using the backup communication link when the service type is not the target service.

[0147] In one embodiment, the apparatus further comprises:

[0148] Frequency acquisition module, used to obtain the operating frequency of the 5G base station and the operating frequency of the preset communication equipment in the substation;

[0149] A frequency adjustment module is used to adjust the operating frequency of the 5G base station when the operating frequency of the 5G base station conflicts with the operating frequency of the communication device.

[0150] In one embodiment, the apparatus further comprises:

[0151] The data acquisition module is further configured to periodically acquire electromagnetic environment data of the 5G base station;

[0152] The signal generation module is used to generate an early warning signal when the electromagnetic environment data reaches a target threshold.

[0153] Each module in the aforementioned 5G base station site selection device within a substation can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor within a computer device in hardware form, or can be stored in a memory within the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0154] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store fault data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for selecting a site for a 5G base station in a substation is implemented.

[0155] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0156] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0157] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0158] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for site selection of a 5G base station in a substation, characterized in that: The method comprises: Acquire historical data of power equipment at various locations within the substation; wherein the historical data includes at least one of the following: operating data, environmental data, and fault data; Selecting characteristic data from each of the historical data; wherein the characteristic data includes data associated with a fault of the power equipment; Inputting the characteristic data into a preset fault prediction model to obtain fault parameters of each of the location points; wherein the fault parameters include at least one of the following: probability of fault occurrence and fault type; Determine candidate location points from the location points based on the fault parameters, and obtain historical electromagnetic field data of each candidate location point; The target location point is determined based on the historical electromagnetic field data of each candidate location point.

2. The method according to claim 1, characterized in that The method for obtaining the fault prediction model includes: Obtain historical operating data, historical fault types, and corresponding fault data for each location point; Identify the failure cycle of each location based on historical failure types and corresponding failure data, and establish an initial failure prediction model using the failure cycle and failure type; Establish sample data based on historical operation data, historical fault types and corresponding fault data; wherein the sample data includes training set data and test set data; The initial fault prediction model is trained using the training set data, and the parameters of the trained initial fault prediction model are adjusted using the test set data to obtain a fault prediction model.

3. The method according to claim 1, characterized in that The method further comprises: Establishing a 5G base station at a target location of the substation; wherein the substation includes a primary communication link and a backup communication link; In the event of a failure of the main communication link of the substation, communication is performed using a backup communication link.

4. The method according to claim 3, characterized in that The substation further includes dedicated network resources, and in the event of a failure of the primary communication link of the substation, communication is performed using a backup communication link, including: When a main communication link of the substation fails, obtaining a service type of each communication service of the substation; When the service type is a target service, switching the communication service to a dedicated network resource for communication; In the case that the service type is not the target service, communication is performed using the backup communication link.

5. The method according to claim 3, characterized in that The method further comprises: Obtain the operating frequency of the 5G base station and the operating frequency of the preset communication equipment in the substation; When the operating frequency of the 5G base station conflicts with the operating frequency of the communication device, the operating frequency of the 5G base station is adjusted.

6. The method according to claim 3, characterized in that The method further comprises: Regularly obtain electromagnetic environment data of the 5G base station; When the electromagnetic environment data reaches a target threshold, an early warning signal is generated.

7. A site selection device for a 5G base station in a substation, characterized in that: The device comprises: A data acquisition module is used to acquire historical data of power equipment at various locations within the substation; wherein the historical data includes at least one of the following: operating data, environmental data, and fault data; A data selection module, configured to select characteristic data from each of the historical data; wherein the characteristic data includes data associated with a fault of the power equipment; A parameter prediction module, configured to input the characteristic data into a preset fault prediction model to obtain fault parameters for each of the location points; wherein the fault parameters include at least one of the following: probability of fault occurrence and fault type; The data acquisition module is further configured to determine a candidate location point from the location points based on the fault parameters, and acquire historical electromagnetic field data of each candidate location point; The location determination module is used to determine the target location point based on the historical electromagnetic field data of each candidate location point.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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