Commutation equipment locating and sizing planning management system and method based on Internet of Things

Abstract

The invention discloses a commutation equipment locating and sizing planning management system and method based on the Internet of Things. The system comprises a data acquisition module, an analysis decision module and a geographic information system module. According to the system provided by the embodiment of the invention, accurate data support is provided for addressing and sizing of commissioning of the commutation equipment by acquiring the operation data, the environment data and the geographical location information of the commutation equipment, the operation state is identified and the fault trend is predicted by using a machine learning algorithm, potential risk points and optimization opportunities are identified, and the accuracy of the commissioning of the commutation equipment is improved. Therefore, various factors possibly influencing the performance of the commissioning commutation equipment can be obtained in the site selection and sizing planning stage, the advantages and disadvantages of different site selection schemes are evaluated, and the accuracy and scientificity of site selection and sizing planning are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a phase-changing equipment site selection, capacity planning and management system and method based on the Internet of Things. Background Art

[0002] As an emerging technology that has developed rapidly in recent years, the Internet of Things technology originated from the integration of traditional automatic identification technology and sensor network technology. Through the combination of sensing devices, communication networks and information processing systems, it realizes the interconnection between objects. With the continuous advancement of sensor technology, communication technology and cloud computing technology, the Internet of Things technology has developed rapidly in the fields of electricity, communication, transportation, etc.

[0003] The site selection, sizing planning and subsequent management of phase-changing equipment (such as power transformers and communication base stations) is a complex and important task. Traditional IoT-based site selection, sizing planning and management methods for phase-changing equipment often rely on manual experience and limited data analysis, which makes it difficult to be accurate and efficient. Summary of the invention

[0004] The present invention provides a phase-changing equipment site selection and capacity planning management system and method based on the Internet of Things to solve the problem that the site selection and capacity planning and subsequent management of the phase-changing equipment are not efficient and accurate enough.

[0005] The present invention provides a phase-changing equipment site selection and capacity planning and management system based on the Internet of Things, the system comprising:

[0006] A data acquisition module, used to collect initial data of the trial-operation phase-changing device, wherein the initial data includes operation data of the trial-operation phase-changing device, environmental data of the target area where the trial-operation phase-changing device is located, and geographical location information;

[0007] An analysis and decision-making module is used to mine and analyze the initial data using a machine learning algorithm to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, and optimize the alternative site selection and capacity determination plan according to the environmental data in the initial data, the operating status and the predicted fault trend to obtain an optimized site selection and capacity determination plan;

[0008] The geographic information system module is used to perform spatial analysis on the optimized site selection and capacity determination plan to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location.

[0009] Optionally, the data acquisition module is specifically used to collect the initial data through a first sensor and an RFID tag pre-installed in the trial operation commutation device and a second sensor in a target area where the trial operation commutation device is located;

[0010] Among them, the first sensor includes a current sensor, a voltage sensor, a temperature sensor and a vibration sensor, and the second sensor includes a temperature and humidity sensor and an air pressure sensor to obtain environmental data. The operating data in the initial data includes current, voltage, temperature and vibration data, and the environmental data in the initial data includes ambient temperature, humidity and air pressure data.

[0011] Optionally, the system further includes a planning management platform and a data transmission processing module, wherein:

[0012] The data transmission processing module is used to pre-process the initial data before the analysis and decision module uses the machine learning algorithm to mine and analyze the initial data, so as to obtain the pre-processed initial data, and transmit it to the planning management platform through the wireless network;

[0013] The planning management platform includes:

[0014] A storage and classification unit, used to store the initial data in a pre-established distributed database, and classify the initial data stored in the distributed database according to data type, timestamp and sensor identification dimension to obtain classified initial data;

[0015] The index establishment and backup unit is used to establish an index for the classified initial data, and to implement a preset data recovery mechanism by backing up the data in the distributed database at preset time intervals.

[0016] Optionally, the system further includes:

[0017] A data set generation module, used for obtaining historical fault records and maintenance logs, and performing standardization processing on the initial data to obtain processed data in a unified format, and then integrating the processed data to obtain a data set before performing mining and analysis on the initial data using a machine learning algorithm to obtain the operating status of the trial-operated commutation device and predict the fault trend;

[0018] A feature data set generation module, used to extract target features from the data set, and integrate the target features with the fault features in the historical fault records to obtain a feature data set including a training set and a test set, wherein the target features include equipment operation features, environmental features, and abnormal fault features;

[0019] An index determination module, used to determine an equipment operation evaluation index, an environmental interference index, and a fault diagnosis index using the data set;

[0020] A model building module, used to build an abnormal classification model based on a neural network model using the feature data set and a machine learning algorithm;

[0021] An abnormality level and threshold preset module, used to determine an abnormality assessment coefficient using the equipment operation evaluation index, the environmental interference index and the fault diagnosis index, and preset an abnormal operation level and a corresponding abnormality threshold using the abnormality assessment coefficient and the historical fault record;

[0022] Wherein, the analysis and decision-making module includes:

[0023] A state and trend determination unit, configured to obtain the operation state and predicted fault trend of the trial operation commutation device based on the abnormal operation level and the abnormal threshold value by using the abnormal classification model and the mining analysis of the initial data;

[0024] The site selection and sizing scheme optimization unit is used to optimize the alternative site selection and sizing schemes by using the environmental data and operating status in the data set and the predicted failure trend to obtain an optimized site selection and sizing scheme.

[0025] Further, using the data set to determine the equipment operation evaluation index and the environmental interference index includes:

[0026] Using the measured values ​​and reference values ​​of the current characteristics, voltage characteristics, temperature characteristics, and vibration characteristics in the data set, respectively determine a current deviation index, a voltage deviation index, a temperature deviation index, and a vibration deviation index, and use the current deviation index, the voltage deviation index, the temperature deviation index, and the vibration deviation index to determine an equipment operation evaluation index;

[0027] The measured values ​​and reference values ​​of the temperature characteristics, humidity characteristics, and air pressure characteristics in the data set are used to determine the ambient temperature deviation index, humidity deviation index, and air pressure deviation index, respectively, and the ambient temperature deviation index, humidity deviation index, and air pressure deviation index are used to determine the environmental interference index.

[0028] Further, determining a fault diagnosis index using the data set includes:

[0029] Determine the reference values ​​of the failure time feature, the failure frequency feature and the maintenance cost feature respectively by using the associated data of the failure time feature, the failure frequency feature and the maintenance cost feature in the data set;

[0030] The reference values ​​are used to respectively determine a failure time deviation, a failure frequency deviation and a maintenance cost deviation, and a failure diagnosis index is determined using the failure time deviation, the failure frequency deviation and the maintenance cost deviation.

[0031] Further, the determining of an abnormality assessment coefficient by using the equipment operation assessment index, the environmental interference index and the fault diagnosis index includes:

[0032] The abnormality assessment coefficient is determined by performing a weighted sum operation on the equipment operation assessment index, the environmental interference index and the fault diagnosis index.

[0033] Optionally, the geographic information system module includes:

[0034] A database generation unit, used to collect geographic information data related to the optimized site selection and capacity determination plan, and generate a geographic database using the geographic information data, wherein the geographic information data includes topography, land use status, transportation network and hydrological information;

[0035] A processing unit, used for cleaning, format conversion, standardization and vectorization of geographic information data in the geographic database to obtain reference geographic data;

[0036] An analysis unit, used for performing a superposition analysis of multiple geographic layers on the optimized site selection and capacity determination scheme by using a geographic information system GIS and reference geographic data in the geographic database to obtain an analysis result;

[0037] A fusion information generating unit is used to determine the fusion information of the optimized site selection and capacity determination plan and the geographical location by using the analysis result.

[0038] Furthermore, the abnormal operation level includes a low abnormal operation level, a medium abnormal operation level and a high abnormal operation level, and the abnormal threshold includes an upper threshold and a lower threshold.

[0039] The present invention provides a method for site selection and capacity planning management of phase-changing equipment based on the Internet of Things, the method comprising:

[0040] Collecting initial data of the trial-operation phase-changing device, wherein the initial data includes operation data of the trial-operation phase-changing device, environmental data of a target area where the trial-operation phase-changing device is located, and geographical location information;

[0041] Using a machine learning algorithm to mine and analyze the initial data to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, and optimizing alternative site selection and sizing solutions based on the environmental data in the initial data, the operating status and the predicted fault trend to obtain an optimized site selection and sizing solution;

[0042] The optimized site selection and capacity determination plan is spatially analyzed to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location.

[0043] The Internet of Things-based phase-changing equipment site selection and sizing planning management system and method provided by the present invention provide accurate data support for the site selection and sizing of trial-operated phase-changing equipment by acquiring the operating data, environmental data and geographical location information of the trial-operated phase-changing equipment, and use machine learning algorithms to identify the operating status and predict failure trends, which helps to identify potential risk points and optimization opportunities. Therefore, various factors that may affect the performance of the trial-operated phase-changing equipment can be obtained in the site selection and sizing planning stage, the advantages and disadvantages of different site selection schemes can be evaluated, and the accuracy and scientificity of site selection and sizing planning can be improved.

[0044] It should be understood that the content described in this section is not intended to identify the key or important features of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 This is a schematic diagram of the structure of a phase-changing equipment site selection and capacity planning and management system based on the Internet of Things according to the first embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of a phase-changing equipment site selection and capacity planning and management system based on the Internet of Things according to Embodiment 2 of the present invention;

[0048] Figure 3 It is a flow chart of a method for site selection, capacity planning and management of phase-changing equipment based on the Internet of Things provided according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In the description of the present invention, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are an "or" relationship. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] Embodiment 1

[0052] Figure 1 The present invention provides a schematic diagram of a phase-changing equipment site selection and capacity planning and management system based on the Internet of Things for the first embodiment of the present invention. This embodiment is applicable to the situation of site selection and capacity planning and management of trial phase-changing equipment. The system can be implemented in the form of hardware and / or software.

[0053] like Figure 1 As shown, the commutation equipment site selection and capacity planning and management system based on the Internet of Things includes a data acquisition module 101, an analysis and decision module 102 and a geographic information system module 103, wherein:

[0054] The data acquisition module is used to collect initial data of the trial-operation phase-changing device, wherein the initial data includes operation data of the trial-operation phase-changing device, environmental data of the target area where the trial-operation phase-changing device is located, and geographical location information;

[0055] The analysis and decision-making module is used to mine and analyze the initial data using a machine learning algorithm to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, and optimize the alternative site selection and capacity determination plan according to the environmental data, the operating status and the predicted fault trend to obtain an optimized site selection and capacity determination plan;

[0056] The geographic information system module is used to perform spatial analysis on the optimized site selection and capacity determination plan to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location;

[0057] The emergency response module is used to monitor the operation data of the trial-operation commutation device in real time and execute the corresponding preset emergency response mechanism according to the operation data;

[0058] The visualization display module is used to visualize the changes in the operating data of the trial-operation commutation device and the fusion information.

[0059] Specifically, the data acquisition module, the analysis and decision-making module and the geographic information system module are connected by electrical signals.

[0060] The data acquisition module can collect initial data of the trial-run commutation equipment and its surrounding environment by pre-deploying various sensors and RFID tags as data acquisition terminals, including the operation data of the commutation equipment (such as current, voltage, temperature and vibration, etc.), environmental data (such as ambient temperature, humidity, air pressure and air quality, etc.) and geographic location information. This setting can realize remote monitoring and real-time data collection, reduce the frequency of manual inspections, improve monitoring efficiency, ensure the comprehensiveness and accuracy of data, and provide a solid data foundation for subsequent analysis.

[0061] The analysis and decision-making module is used to integrate the initial data obtained after preprocessing, use machine learning algorithms to mine and analyze the initial data, evaluate the operating status of the trial-run commutation equipment and predict the fault trend, and optimize the site selection and capacity determination plan based on environmental data, operating status and predicted fault trend, so as to improve the accuracy and scientificity of site selection and capacity determination and reduce planning errors and cost waste caused by environmental factors;

[0062] The geographic information system module is used to integrate geographic information, combine the optimized site selection and capacity determination plan with the geographical location, conduct spatial analysis, assist in the geographical location selection, and improve the accuracy and efficiency of planning.

[0063] Optionally, the IoT-based phase-change equipment site selection and capacity planning and management system may also include:

[0064] The emergency response module is used to monitor the operation status of equipment in real time, detect abnormalities and faults, trigger the alarm mechanism, start the emergency response process, promptly discover and deal with potential problems, reduce the impact of faults on system operation, and ensure the safe and stable operation of the power grid;

[0065] The visualization display module is used to display the operating status of the trial-run phase-changing equipment, changes in environmental data, and the site selection and capacity planning results in the optimized site selection and capacity planning plan in the form of charts, etc., to facilitate management personnel's understanding and decision-making, improve information transparency, and enhance decision-making support capabilities.

[0066] The Internet of Things-based phase-changing equipment site selection and capacity planning and management system provided in the embodiment of the present invention provides accurate data support for the site selection and capacity planning of trial-operated phase-changing equipment by acquiring the operating data, environmental data and geographical location information of the trial-operated phase-changing equipment, and uses a machine learning algorithm to identify the operating status and predict the failure trend, which helps to identify potential risk points and optimization opportunities. Therefore, various factors that may affect the performance of the trial-operated phase-changing equipment can be obtained in the site selection and capacity planning stage, the advantages and disadvantages of different site selection schemes can be evaluated, and the accuracy and scientificity of site selection and capacity planning can be improved.

[0067] Embodiment 2

[0068] Figure 2 A structural diagram of a phase-changing equipment site selection and capacity planning and management system based on the Internet of Things is provided in Example 2 of the present invention. The system of the embodiment of the present invention is further optimized on the basis of the above system, and provides a specific method for site selection, capacity planning and management of trial-operated phase-changing equipment.

[0069] like Figure 2 As shown, the system includes a data acquisition module 201, a data transmission processing module 202, an analysis and decision module 203, a geographic information system module 204, an emergency response module 205 and a visualization display module 206, and a planning management platform 207 that is communicatively connected to the above modules, wherein the geographic information system module 204, the emergency response module 205 and the visualization display module 206 are communicatively connected, and the analysis and decision module 203 and the geographic information system module 204 are communicatively connected.

[0070] Optionally, the data acquisition module is specifically used to collect the initial data through a first sensor and an RFID tag pre-installed in the trial operation commutation device and a second sensor in a target area where the trial operation commutation device is located;

[0071] Among them, the first sensor includes a current sensor, a voltage sensor, a temperature sensor and a vibration sensor, and the second sensor includes a temperature and humidity sensor and an air pressure sensor to obtain environmental data. The operating data in the initial data includes current, voltage, temperature and vibration data, and the environmental data in the initial data includes ambient temperature, humidity and air pressure data.

[0072] Specifically, current sensors, voltage sensors, temperature sensors, vibration sensors, etc. can be installed on the trial-operation phase-changing equipment, so that the data acquisition module can monitor the operation data of the trial-operation phase-changing equipment in real time. Temperature and humidity sensors and air pressure sensors, etc. can be arranged in the surrounding environment of the trial-operation phase-changing equipment, so that the data acquisition module can obtain environmental data, and RFID tags can be deployed on the trial-operation phase-changing equipment, so that the data acquisition module can identify the trial-operation phase-changing equipment. Combined with various sensors installed on the trial-operation phase-changing equipment, the data acquisition module can obtain the operation data and location information of the equipment, where the operation data includes current, voltage, temperature and vibration data, and the environmental data includes ambient temperature, humidity and air pressure data. The data collected by the sensor is pre-processed, including data verification, filtering and denoising, and data compression.

[0073] Optionally, the system further includes a planning management platform and a data transmission processing module, wherein:

[0074] The data transmission processing module is used to pre-process the initial data before the analysis and decision module uses the machine learning algorithm to mine and analyze the initial data, so as to obtain the pre-processed initial data, and transmit it to the planning management platform through the wireless network, wherein the pre-processing includes data cleaning, compression and encryption processing;

[0075] The planning management platform includes:

[0076] A storage and classification unit, used to store the initial data in a pre-established distributed database, and classify the initial data stored in the distributed database according to data type, timestamp and sensor identification dimension to obtain classified initial data;

[0077] The index establishment and backup unit is used to establish an index for the classified initial data, and to implement a preset data recovery mechanism by backing up the data in the distributed database at preset time intervals.

[0078] Specifically, the data transmission processing module is used to perform preliminary data cleaning, compression, and encryption processing to ensure the security and efficient transmission of data, ensure the stability and security of data transmission, reduce losses and interference during data transmission, and improve data quality and transmission efficiency. The data transmission processing module can decode the data of the RFID tag, read the information recorded in the tag, timestamp the processed data, and synchronize it with the real-time status of the trial operation phase-changing equipment to ensure the timing and traceability of the data. The data transmission processing module can encapsulate the pre-processed data, including timestamp, sensor identification, and data type information. Encrypt the encapsulated data using an encryption algorithm to ensure the security of the data during transmission, and transmit it to the planning management platform via a wireless network (such as Wi-Fi, 4G / 5G or LoRa, etc.), using encryption technology to ensure the security of the data and prevent data leakage or tampering.

[0079] The planning management platform can classify, store and back up the collected data, and provide a data query interface to facilitate the long-term preservation and rapid retrieval of data, and facilitate data analysis. The planning management platform can establish a distributed database to support the rapid storage and retrieval of large-scale data. After the planning management platform receives the pre-processed data, it stores it in a distributed database. By classifying the data stored in the distributed database according to the data type, timestamp and sensor identification dimensions, and indexing the classified data, this setting can improve data query efficiency. Regularly backing up data in the distributed database can prevent data loss or damage, and by setting up a data recovery mechanism, ensure that data can be quickly restored when data is lost or damaged;

[0080] Optionally, the system further includes:

[0081] A data set generation module, used for obtaining historical fault records and maintenance logs, and performing standardization processing on the initial data to obtain processed data in a unified format, and then integrating the processed data to obtain a data set before performing mining and analysis on the initial data using a machine learning algorithm to obtain the operating status of the trial-operated commutation device and predict the fault trend;

[0082] A feature data set generation module, used to extract target features from the data set, and integrate the target features with the fault features in the historical fault records to obtain a feature data set including a training set and a test set, wherein the target features include equipment operation features, environmental features, and abnormal fault features;

[0083] An index determination module, used to determine an equipment operation evaluation index, an environmental interference index, and a fault diagnosis index using the data set;

[0084] A model building module, used to build an abnormal classification model based on a neural network model using the feature data set and a machine learning algorithm;

[0085] An abnormality level and threshold preset module, used to determine an abnormality assessment coefficient using the equipment operation evaluation index, the environmental interference index and the fault diagnosis index, and preset an abnormal operation level and a corresponding abnormality threshold using the abnormality assessment coefficient and the historical fault record;

[0086] Wherein, the analysis and decision-making module includes:

[0087] A state and trend determination unit, configured to obtain the operation state and predicted fault trend of the trial operation commutation device based on the abnormal operation level and the abnormal threshold value by using the abnormal classification model and the mining analysis of the initial data;

[0088] The site selection and sizing scheme optimization unit is used to optimize the alternative site selection and sizing schemes by using the environmental data and operating status in the data set and the predicted failure trend to obtain an optimized site selection and sizing scheme.

[0089] Specifically, the data set generation module can extract operating data and environmental data of a fixed analysis cycle, and obtain historical fault records and maintenance logs, convert data from different sources and formats into a unified format, perform standardized processing, and integrate the acquired data to form a unified data set for subsequent analysis, where historical fault records include fault type, fault time, fault frequency and repair cost.

[0090] The feature data set generation module can extract target features for evaluating the operating status of the commutation equipment and predicting the fault trend from the data set, specifically including equipment operating features and environmental features. The feature data set generation module can mark the (abnormal) fault features in the historical fault records, merge them with the target features to obtain a feature data set for model training, and divide the feature data set into two subsets, namely a training set and a test set. Among them, extracting target features for evaluating the operating status of the commutation equipment and predicting the fault trend from the data set includes: performing feature analysis on the operating data to obtain equipment operating features such as current features, voltage features, temperature features and vibration features; performing feature analysis on the environmental data to obtain environmental features such as environmental temperature features, humidity features and air pressure features. Fault features include fault time features, fault frequency features and maintenance cost features associated with the fault type.

[0091] The index determination module can analyze the operating data in the data set, calculate the equipment operation evaluation index, analyze the operating status and trend of the commutation equipment, and analyze the environmental data of the same fixed analysis period in the data set, calculate the environmental interference index, analyze the changing trend of the environment around the commutation equipment, and then calculate the fault diagnosis index based on the operating data, environmental data and fault characteristics.

[0092] The model building module can use machine learning algorithms, combined with equipment operation characteristics, environmental characteristics and fault characteristics in the feature data set, to build an anomaly classification model based on a neural network model, and use relevant data from the test set to test and analyze the anomaly classification model.

[0093] The abnormal level and threshold preset module can use the equipment operation evaluation index, environmental interference index and fault diagnosis index to obtain the abnormal evaluation coefficient, and analyze the operating status of the commutation equipment. Based on the abnormal evaluation coefficient and historical fault records, the abnormal operation level and its corresponding abnormal threshold are preset.

[0094] The analysis and decision-making module can use the trained abnormal classification model and analyze the real-time data of the phase-changing equipment to evaluate the current operating status of the phase-changing equipment, identify the early signs of performance degradation and abnormalities of the phase-changing equipment, and predict the failure trend. Specifically, it includes: combining the collected environmental data to analyze the impact of different environmental data on the operation of the phase-changing equipment, and using the abnormal classification model to evaluate the operating status of the phase-changing equipment, combining the abnormal operation level and its corresponding abnormal threshold to analyze the candidate site selection and sizing schemes, and comprehensively evaluate the advantages and disadvantages of each scheme to optimize the site selection and sizing scheme;

[0095] Further, using the data set to determine the equipment operation evaluation index and the environmental interference index includes:

[0096] Using the measured values ​​and reference values ​​of the current characteristics, voltage characteristics, temperature characteristics, and vibration characteristics in the data set, respectively determine a current deviation index, a voltage deviation index, a temperature deviation index, and a vibration deviation index, and use the current deviation index, the voltage deviation index, the temperature deviation index, and the vibration deviation index to determine an equipment operation evaluation index;

[0097] The measured values ​​and reference values ​​of the temperature characteristics, humidity characteristics, and air pressure characteristics in the data set are used to determine the ambient temperature deviation index, humidity deviation index, and air pressure deviation index, respectively, and the ambient temperature deviation index, humidity deviation index, and air pressure deviation index are used to determine the environmental interference index.

[0098] Exemplary methods for determining the equipment operation evaluation index include:

[0099] EA=P C ×PV ×P T ×P R ;

[0100]

[0101] Among them, EA is the equipment operation evaluation index, P C is the current deviation index, P V is the voltage deviation index, P T is the temperature deviation index, P R is the vibration deviation index, C i is the current value of the ith measurement cycle, V i is the voltage value of the ith measurement cycle, T i is the temperature value of the i-th measurement cycle, R i is the vibration value of the ith measurement cycle, C base is the reference value of the current, V base is the reference value of voltage, T base is the reference value of temperature, R base is the reference value of vibration, and n is the total number of measurement cycles. When the operating data of the commutation equipment is close to its reference value, EA tends to a smaller value, indicating that the equipment is in good operating condition. When the operating data of the commutation equipment deviates significantly from the reference value, EA tends to a larger value, indicating that there may be problems with the equipment. By calculating the sum of the squares of the deviations of each data from its reference value and evaluating the degree of deviation of the data, data that deviates from the normal operating range can be identified. The influence of vibration data is introduced in the form of a score. The larger the vibration deviation, the closer the score is to 1, which means that the vibration contributes less to EA.

[0102] The environmental interference index is determined by:

[0103] EI=P E ×P W ×P P ;

[0104]

[0105] Among them, EI is the environmental interference index, P E is the ambient temperature deviation index, P W is the humidity deviation index, P P is the pressure deviation index, E i is the ambient temperature value of the i-th measurement cycle, W i is the ambient humidity value of the ith measurement cycle, P i is the ambient air pressure value of the ith measurement cycle, E base is the reference value of ambient temperature, W base is the reference value of ambient humidity, P baseis the reference value of ambient air pressure, σ E is the standard deviation of the ambient temperature, σ W is the standard deviation of ambient humidity, and n is the total number of measurement cycles. The value range of EI is between 0 and 1. When the environmental data is close to its reference value, EI tends to 1, indicating that the environmental interference is small and the environment is stable. When the environmental data deviates significantly from the reference value, EI tends to 0, indicating that the environmental interference is large and the environment changes drastically. Use exponential function By simulating the Gaussian distribution characteristics of humidity deviation from the baseline value, the significance of humidity changes can be evaluated. In this form, the nonlinear effect of air pressure change on the environmental disturbance index is simulated.

[0106] Further, determining a fault diagnosis index using the data set includes:

[0107] Determine the reference values ​​of the failure time feature, the failure frequency feature and the maintenance cost feature respectively by using the associated data of the failure time feature, the failure frequency feature and the maintenance cost feature in the data set;

[0108] The reference values ​​are used to respectively determine a failure time deviation, a failure frequency deviation and a maintenance cost deviation, and a failure diagnosis index is determined using the failure time deviation, the failure frequency deviation and the maintenance cost deviation.

[0109] Further, the determining of an abnormality assessment coefficient by using the equipment operation assessment index, the environmental interference index and the fault diagnosis index includes:

[0110] The abnormality assessment coefficient is determined by performing a weighted sum operation on the equipment operation assessment index, the environmental interference index and the fault diagnosis index.

[0111] Exemplarily, the fault diagnosis index and the abnormality assessment coefficient are determined in the following manner:

[0112]

[0113] Among them, AP is the abnormal assessment coefficient, EA is the equipment operation assessment index, EI is the environmental interference index, PI is the fault diagnosis index, which reflects the fault condition of the equipment, TF is the fault time, FF is the fault frequency, MC is the maintenance cost, TF is the fault time, FF is the fault frequency, MC is the maintenance cost, TF is the fault time, FF is the fault frequency, MC is the maintenance cost, TF is the fault time, FF is the fault frequency, MC is the maintenance cost, TF is the fault time, FF is the fault frequency, MC is the maintenance cost, TF is the fault time, FF is the fault frequency, MC is the maintenance cost, TF is the fault time, TF is the fault frequency, MC is the maintenance cost ... base , FF base and MC base are the benchmark values ​​of failure time, frequency and maintenance cost respectively, σ TF is the standard deviation of failure time, w 1 、w 2 and w 3is a weight coefficient used to adjust the relative importance of EA, EI, and PI in AP calculation. A lower AP value indicates that the device is in good operating condition, the environment is stable, and the fault characteristics are not obvious. A higher AP value indicates that the device may have operating problems, the environment is more disturbed, or there are obvious fault characteristics. 1 、w 2 and w 3 EA, EI and PI are weighted separately to reflect their relative importance in the overall assessment. The value range of PI is greater than or equal to 0, where 0 means no deviation, that is, the equipment is operating normally. The larger the PI value, the greater the deviation of the equipment's operating status from the normal baseline, and the higher the risk of failure.

[0114] Optionally, the geographic information system module includes:

[0115] A database generation unit, used to collect geographic information data related to the optimized site selection and capacity determination plan, and generate a geographic database using the geographic information data, wherein the geographic information data includes topography, land use status, transportation network and hydrological information;

[0116] A processing unit, used for cleaning, format conversion, standardization and vectorization of geographic information data in the geographic database to obtain reference geographic data;

[0117] An analysis unit, used for performing a superposition analysis of multiple geographic layers on the optimized site selection and capacity determination scheme by using a geographic information system GIS and reference geographic data in the geographic database to obtain an analysis result;

[0118] A fusion information generating unit is used to determine the fusion information of the optimized site selection and capacity determination plan and the geographical location by using the analysis result.

[0119] Specifically, the database generation unit can collect geographic information data related to the optimization of site selection and capacity determination plan from different sources to form a geographic database to provide data support for subsequent spatial analysis. Geographic information data specifically includes topography, land use status, transportation network and hydrological information. Sources include satellite remote sensing, drone aerial photography, GPS positioning and ground measurement data.

[0120] The processing unit can clean, convert and standardize the collected geographic information data to ensure the consistency and accuracy of the data, and vectorize the geographic information data to facilitate subsequent analysis and processing by the geographic information system (GIS).

[0121] The analysis unit can use the geographic information system GIS and the reference geographic data in the geographic database to perform spatial analysis on the geographic location information of the optimized site selection and capacity determination plan, overlay the plan with various layers in the geographic database for analysis, set a buffer zone for the site selection point, analyze the environmental influencing factors within the buffer zone, and combine the site selection and capacity determination plan with the geographic information, thereby obtaining fusion information of the optimized site selection and capacity determination plan and the geographic location.

[0122] Furthermore, the abnormal operation level includes a low abnormal operation level, a medium abnormal operation level and a high abnormal operation level, and the abnormal threshold includes an upper threshold and a lower threshold.

[0123] Exemplarily, the abnormal operation level and the abnormal threshold may satisfy the following relationship:

[0124] When AP≤AP MY When , it is a low abnormal operation level;

[0125] When AP MY <AP≤AP HY When , it is the medium abnormal operation level;

[0126] When AP>AP HY , it is a high abnormal operation level.

[0127] Among them, AP is the abnormal assessment coefficient, AP MY is the upper threshold corresponding to the low abnormal operation level and the lower threshold corresponding to the medium abnormal operation level, AP HY The upper threshold corresponding to the medium abnormal operation level and the lower threshold corresponding to the high abnormal operation level.

[0128] Furthermore, the emergency response module includes:

[0129] an abnormality and fault information determination unit, configured to analyze the operation data of the trial operation commutation device using the abnormality assessment coefficient to determine abnormality and fault information, and send the abnormality and fault information to the alarm information generation unit, wherein the abnormality and fault information includes a target abnormal operation level;

[0130] an alarm information generating unit, configured to generate and send alarm information by triggering a first preset emergency response mechanism upon receiving the abnormality and fault information sent by the abnormality and fault information determining unit;

[0131] The emergency response unit is used to execute a corresponding second preset emergency response mechanism according to the target abnormal operation level and the alarm information.

[0132] Specifically, the abnormality and fault information determination unit can be integrated into various sensors and monitoring devices in the power grid equipment to collect the operating status data of the phase-changing equipment in real time, and transmit the data to the planning and management platform through the Internet of Things. It can also combine the abnormality assessment coefficient to analyze the operating status of the phase-changing equipment, compare the operating data with the corresponding abnormality threshold, and identify abnormalities and fault information.

[0133] If the alarm information generating unit receives the abnormality and fault information sent by the abnormality and fault information determining unit, it generates and sends the alarm information by triggering the first preset emergency response mechanism, such as sending it to relevant personnel through text messages and mobile applications.

[0134] The emergency response unit can start the emergency response process corresponding to the current target abnormal operation level based on the abnormal operation level, and match the corresponding emergency plan according to the alarm information and operation status, and execute the corresponding second preset emergency response mechanism to promptly discover and deal with potential problems.

[0135] The IoT-based phase-changing equipment site selection and sizing planning and management system provided in the embodiment of the present invention can not only evaluate the current status of the equipment in real time, but also predict potential failure trends and realize predictive maintenance by deploying various sensors, collecting equipment operation data, and combining machine learning algorithms. It can reduce unexpected downtime, reduce maintenance costs, and improve the operating efficiency and life of the equipment. In addition, by comprehensively considering equipment operation data, environmental data, and historical failure data, it provides the optimal solution for site selection and sizing planning, analyzes the cost-effectiveness of different site selection solutions, and effectively reduces construction and operation costs.

[0136] Embodiment 3

[0137] Figure 3 A flowchart of a method for site selection, capacity planning and management of phase-changing equipment based on the Internet of Things is provided for the third embodiment of the present invention. This embodiment is applicable to the site selection, capacity planning and subsequent management of phase-changing equipment.

[0138] like Figure 3 As shown, the third embodiment of the present invention provides a method for site selection and capacity planning and management of phase-changing equipment based on the Internet of Things, which specifically includes the following steps:

[0139] S301. Collect initial data of a trial-operation phase-changing device, wherein the initial data includes operation data of the trial-operation phase-changing device, environmental data of a target area where the trial-operation phase-changing device is located, and geographical location information.

[0140] S302. Use a machine learning algorithm to mine and analyze the initial data to obtain the operating status and predicted fault trend of the trial-operated phase-changing equipment, and optimize alternative site selection and sizing plans based on the environmental data in the initial data, the operating status and the predicted fault trend to obtain an optimized site selection and sizing plan.

[0141] S303: Perform spatial analysis on the optimized site selection and capacity determination plan to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location.

[0142] The technical solution of the embodiment of the present invention provides accurate data support for the site selection and capacity determination of the trial operation phase-changing equipment by acquiring the operation data, environmental data and geographical location information of the phase-changing equipment, and uses a machine learning algorithm to identify the operation status and predict the failure trend, which helps to identify potential risk points and optimization opportunities. Therefore, various factors that may affect the performance of the trial operation phase-changing equipment can be obtained in the site selection and capacity planning stage, the advantages and disadvantages of different site selection schemes can be evaluated, and the accuracy and scientificity of the site selection and capacity planning can be improved.

[0143] Optionally, the collecting of initial data of the trial operation commutation device includes:

[0144] The initial data is collected by a first sensor and an RFID tag pre-installed in the trial operation commutation device and a second sensor in the target area where the trial operation commutation device is located; wherein the first sensor includes a current sensor, a voltage sensor, a temperature sensor and a vibration sensor, and the second sensor includes a temperature and humidity sensor and an air pressure sensor to obtain environmental data, the operating data in the initial data includes current, voltage, temperature and vibration data, and the environmental data in the initial data includes environmental temperature, humidity and air pressure data.

[0145] Optionally, the above method further includes:

[0146] Before using the machine learning algorithm to mine and analyze the initial data, preprocessing the initial data to obtain preprocessed initial data, and transmitting the preprocessed initial data to the planning management platform via a wireless network;

[0147] Wherein, the planning management platform is used for:

[0148] The initial data is stored in a pre-established distributed database, and the initial data stored in the distributed database is classified according to the data type, timestamp and sensor identification dimension to obtain the classified initial data; an index is established for the classified initial data, and a preset data recovery mechanism is implemented by backing up the data in the distributed database at preset time intervals.

[0149] Optionally, the above method further includes:

[0150] Before using the machine learning algorithm to mine and analyze the initial data to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, historical fault records and maintenance logs are obtained, and the initial data are standardized to obtain processed data in a unified format, and then the processed data are integrated to obtain a data set; target features are extracted from the data set, and the target features and the fault features in the historical fault records are integrated to obtain a feature data set including a training set and a test set, wherein the target features include equipment operation features and environmental features; the data set is used to determine an equipment operation evaluation index, an environmental interference index, and a fault diagnosis index; the feature data set and the machine learning algorithm are used to construct an abnormal classification model based on a neural network model; the equipment operation evaluation index, the environmental interference index, and the fault diagnosis index are used to determine an abnormal evaluation coefficient, and the abnormal operation level and the corresponding abnormal threshold are preset using the abnormal evaluation coefficient and the historical fault records;

[0151] The method of mining and analyzing the initial data using a machine learning algorithm to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, and optimizing the alternative site selection and capacity determination scheme according to the environmental data in the initial data, the operating status and the predicted fault trend to obtain the optimized site selection and capacity determination scheme includes:

[0152] Based on the abnormal operation level and the abnormal threshold, the abnormal classification model and the initial data are used to mine and analyze the operation status and predicted fault trend of the trial operation phase-changing equipment; the environmental data and operation status in the data set and the predicted fault trend are used to optimize the alternative site selection and sizing scheme to obtain the optimized site selection and sizing scheme.

[0153] Further, using the data set to determine the equipment operation evaluation index and the environmental interference index includes:

[0154] Using the measured values ​​and reference values ​​of the current characteristics, voltage characteristics, temperature characteristics, and vibration characteristics in the data set, the current deviation index, voltage deviation index, temperature deviation index, and vibration deviation index are respectively determined, and the equipment operation evaluation index is determined using the current deviation index, the voltage deviation index, the temperature deviation index, and the vibration deviation index; using the measured values ​​and reference values ​​of the temperature characteristics, humidity characteristics, and air pressure characteristics in the data set, the ambient temperature deviation index, humidity deviation index, and air pressure deviation index are respectively determined, and the environmental interference index is determined using the ambient temperature deviation index, the humidity deviation index, and the air pressure deviation index.

[0155] Further, determining a fault diagnosis index using the data set includes:

[0156] Using the associated data of the fault time feature, the fault frequency feature and the maintenance cost feature in the data set, the benchmark values ​​of the fault time feature, the fault frequency feature and the maintenance cost feature are determined respectively; using the benchmark values, the fault time deviation, the fault frequency deviation and the maintenance cost deviation are determined respectively, and the fault diagnosis index is determined using the fault time deviation, the fault frequency deviation and the maintenance cost deviation.

[0157] Furthermore, the use of the equipment operation evaluation index, the environmental interference index and the fault diagnosis index to determine the abnormality evaluation coefficient includes: determining the abnormality evaluation coefficient by performing a weighted sum operation on the equipment operation evaluation index, the environmental interference index and the fault diagnosis index.

[0158] Optionally, the performing spatial analysis on the optimized site selection and capacity determination plan to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location includes:

[0159] Collect geographic information data related to the optimized site selection and capacity determination plan, and use the geographic information data to generate a geographic database, wherein the geographic information data includes topography, land use status, transportation network and hydrological information; clean, format convert, standardize and vectorize the geographic information data in the geographic database to obtain reference geographic data; use the geographic information system GIS and the reference geographic data in the geographic database to perform multi-geographic layer overlay analysis on the optimized site selection and capacity determination plan to obtain analysis results; use the analysis results to determine the fusion information of the optimized site selection and capacity determination plan and the geographic location.

[0160] Furthermore, the abnormal operation level includes a low abnormal operation level, a medium abnormal operation level and a high abnormal operation level, and the abnormal threshold includes an upper threshold and a lower threshold.

Claims

1. A phase-changing equipment site selection and capacity planning and management system based on the Internet of Things, characterized in that: include: A data acquisition module, used to collect initial data of the trial-operation phase-changing device, wherein the initial data includes operation data of the trial-operation phase-changing device, environmental data of the target area where the trial-operation phase-changing device is located, and geographical location information; An analysis and decision-making module is used to mine and analyze the initial data using a machine learning algorithm to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, and optimize the alternative site selection and capacity determination plan according to the environmental data in the initial data, the operating status and the predicted fault trend to obtain an optimized site selection and capacity determination plan; The geographic information system module is used to perform spatial analysis on the optimized site selection and capacity determination plan to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location.

2. The system according to claim 1, characterized in that The data acquisition module is specifically used to collect the initial data through a first sensor and an RFID tag pre-installed in the trial operation commutation device and a second sensor in a target area where the trial operation commutation device is located; Among them, the first sensor includes a current sensor, a voltage sensor, a temperature sensor and a vibration sensor, and the second sensor includes a temperature and humidity sensor and an air pressure sensor to obtain environmental data. The operating data in the initial data includes current, voltage, temperature and vibration data, and the environmental data in the initial data includes ambient temperature, humidity and air pressure data.

3. The system according to claim 1, characterized in that The system also includes a planning management platform and a data transmission processing module, wherein: The data transmission processing module is used to pre-process the initial data before the analysis and decision module uses the machine learning algorithm to mine and analyze the initial data, so as to obtain the pre-processed initial data, and transmit it to the planning management platform through the wireless network; The planning management platform includes: A storage and classification unit, used to store the initial data in a pre-established distributed database, and classify the initial data stored in the distributed database according to data type, timestamp and sensor identification dimension to obtain classified initial data; The index establishment and backup unit is used to establish an index for the classified initial data, and to implement a preset data recovery mechanism by backing up the data in the distributed database at preset time intervals.

4. The system according to any one of claims 1 to 3, characterized in that: The system further comprises: A data set generation module, used for obtaining historical fault records and maintenance logs, and performing standardization processing on the initial data to obtain processed data in a unified format, and then integrating the processed data to obtain a data set before performing mining and analysis on the initial data using a machine learning algorithm to obtain the operating status of the trial-operated commutation device and predict the fault trend; A feature data set generation module, used to extract target features from the data set, and integrate the target features with the fault features in the historical fault records to obtain a feature data set including a training set and a test set, wherein the target features include equipment operation features and environmental features; An index determination module, used to determine an equipment operation evaluation index, an environmental interference index, and a fault diagnosis index using the data set; A model building module, used to build an abnormal classification model based on a neural network model using the feature data set and a machine learning algorithm; An abnormality level and threshold preset module, used to determine an abnormality assessment coefficient using the equipment operation evaluation index, the environmental interference index and the fault diagnosis index, and preset an abnormal operation level and a corresponding abnormality threshold using the abnormality assessment coefficient and the historical fault record; Wherein, the analysis and decision-making module includes: A state and trend determination unit, configured to obtain the operation state and predicted fault trend of the trial operation commutation device based on the abnormal operation level and the abnormal threshold value by using the abnormal classification model and the mining analysis of the initial data; The site selection and sizing scheme optimization unit is used to optimize the alternative site selection and sizing schemes by using the environmental data and operating status in the data set and the predicted failure trend to obtain an optimized site selection and sizing scheme.

5. The system according to claim 4, characterized in that The data set is used to determine an equipment operation evaluation index and an environmental interference index, including: Using the measured values ​​and reference values ​​of the current characteristics, voltage characteristics, temperature characteristics, and vibration characteristics in the data set, respectively determine a current deviation index, a voltage deviation index, a temperature deviation index, and a vibration deviation index, and use the current deviation index, the voltage deviation index, the temperature deviation index, and the vibration deviation index to determine an equipment operation evaluation index; The measured values ​​and reference values ​​of the temperature characteristics, humidity characteristics, and air pressure characteristics in the data set are used to determine the ambient temperature deviation index, humidity deviation index, and air pressure deviation index, respectively, and the ambient temperature deviation index, humidity deviation index, and air pressure deviation index are used to determine the environmental interference index.

6. The system according to claim 4, characterized in that Determining a fault diagnosis index using the data set includes: Determine the reference values ​​of the failure time feature, the failure frequency feature and the maintenance cost feature respectively by using the associated data of the failure time feature, the failure frequency feature and the maintenance cost feature in the data set; The reference values ​​are used to respectively determine a failure time deviation, a failure frequency deviation and a maintenance cost deviation, and a failure diagnosis index is determined using the failure time deviation, the failure frequency deviation and the maintenance cost deviation.

7. The system according to claim 4, characterized in that The determining of an abnormality assessment coefficient by using the equipment operation assessment index, the environmental interference index and the fault diagnosis index includes: The abnormality assessment coefficient is determined by performing a weighted sum operation on the equipment operation assessment index, the environmental interference index and the fault diagnosis index.

8. The system according to claim 1, characterized in that The geographic information system module comprises: A database generation unit, used to collect geographic information data related to the optimized site selection and capacity determination plan, and generate a geographic database using the geographic information data, wherein the geographic information data includes topography, land use status, transportation network and hydrological information; A processing unit, used for cleaning, format conversion, standardization and vectorization of geographic information data in the geographic database to obtain reference geographic data; An analysis unit, used for performing a superposition analysis of multiple geographic layers on the optimized site selection and capacity determination scheme by using a geographic information system GIS and reference geographic data in the geographic database to obtain an analysis result; A fusion information generating unit is used to determine the fusion information of the optimized site selection and capacity determination plan and the geographical location by using the analysis result.

9. The system according to claim 4, characterized in that The abnormal operation level includes a low abnormal operation level, a medium abnormal operation level and a high abnormal operation level, and the abnormal threshold includes an upper threshold and a lower threshold.

10. A method for site selection and capacity planning and management of commutation equipment based on the Internet of Things, characterized in that: include: Collecting initial data of the trial-operation phase-changing device, wherein the initial data includes operation data of the trial-operation phase-changing device, environmental data of a target area where the trial-operation phase-changing device is located, and geographical location information; Using a machine learning algorithm to mine and analyze the initial data to obtain the operating status and predicted fault trend of the trial-operated commutation equipment, and optimizing alternative site selection and sizing solutions based on the environmental data in the initial data, the operating status and the predicted fault trend to obtain an optimized site selection and sizing solution; The optimized site selection and capacity determination plan is spatially analyzed to obtain fusion information of the optimized site selection and capacity determination plan and the geographical location.

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