Electric power engineering field management system based on mobile terminal

Abstract

The invention discloses an electric power engineering field management system based on a mobile terminal, and the system comprises a data collection module which is used for collecting the engineering progress data, equipment state data and environment parameter data of a field in real time through a sensor and communication equipment which are deployed in the electric power engineering field; the data transmission module is connected with the data acquisition module and is used for transmitting the acquired data to a cloud server in real time through a mobile network; the cloud server is connected with the data transmission module and is used for processing the data after receiving the data and generating an analysis report; and the mobile terminal is connected with the cloud server and is used for feeding back the analysis report to a field worker, monitoring the field safety condition in real time and immediately giving out an early warning after an abnormality is found. The real-time performance and the response speed of field management are improved, the efficiency of data sharing and cooperative work is enhanced, and the information island phenomenon is reduced. And by using the convenience of the mobile terminal, the working efficiency and the flexibility of field staff are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power engineering management, and in particular relates to a power engineering site management system based on a mobile terminal. Background Art

[0002] As the scale and complexity of power projects continue to expand, traditional on-site management methods can no longer meet the needs of modern project management. Existing power project on-site management systems mostly rely on fixed workstations and PC software, which have the following defects:

[0003] Poor mobility: Field staff cannot access management information anytime and anywhere, which limits the flexibility of on-site decision-making. The lack of effective mobile terminal applications makes it difficult for field staff to use mobile devices to work.

[0004] Information is not updated in a timely manner: On-site data collection and processing capabilities are limited, making it impossible to achieve real-time monitoring and rapid analysis; information transmission relies on manual updates, which can easily lead to information lags and affect the accuracy of decision-making.

[0005] Difficulty in data sharing: Information exchange between on-site staff and the management center is not smooth, resulting in slow response; there are obstacles to information sharing between different departments and teams, which affects the efficiency of collaborative work.

[0006] Safety hazards: The on-site environment is complex, and traditional management methods are difficult to effectively monitor the safety of staff. The safety monitoring method is single and cannot fully guarantee the safety of on-site staff. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a power engineering site management system based on a mobile terminal, comprising:

[0008] The data acquisition module is used to collect on-site engineering progress data, equipment status data, and environmental parameter data in real time through sensors and communication equipment deployed at the power engineering site;

[0009] A data transmission module, connected to the data acquisition module, for transmitting the collected data to the cloud server in real time via a mobile network;

[0010] A cloud server, connected to the data transmission module, is used to process the data after receiving it and generate an analysis report;

[0011] The mobile terminal is connected to the cloud server and is used to feed back the analysis report to the on-site staff, monitor the on-site safety status in real time, and issue an early warning immediately after an abnormality is found.

[0012] Preferably, the data acquisition module includes a first acquisition unit, a second acquisition unit, and a third acquisition unit;

[0013] The first acquisition unit is used to collect equipment status data at the power engineering site based on distributed sensors;

[0014] The second acquisition unit is used to obtain the surrounding environment data of the power engineering site;

[0015] The third acquisition unit is used to obtain engineering progress data through regular and continuous monitoring.

[0016] Preferably, the distributed sensors include but are not limited to current and voltage sensors, temperature sensors, vibration sensors, acoustic wave sensors, infrared imaging sensors, dissolved gas sensors in oil, distributed optical fiber sensors, partial discharge sensors, MEMS sensors, magnetoresistive current sensors, and ultra-high frequency sensors;

[0017] The second acquisition unit includes at least a temperature and humidity sensor, an electrochemical sensor, and an underground pipeline data acquisition unit;

[0018] The temperature and humidity sensor is used to monitor the temperature and humidity of the environment surrounding the equipment on the power engineering site;

[0019] The electrochemical sensor is used to monitor a specific gas environment by reacting with the gas in the measured medium to generate an electrical signal proportional to the gas concentration;

[0020] The underground pipeline data acquisition unit is used to conduct detailed detection of the location, direction, and buried depth of the underground pipeline and record the attribute information of the pipeline.

[0021] Preferably, the data transmission module includes a positioning unit, a data transmission unit, a data uploading unit, and an encryption unit;

[0022] The positioning unit uses STM32 as the main control and combines GPS or Beidou satellite to obtain positioning information;

[0023] The data transmission unit transmits data in a push-pull or push manner based on NB-IoT network technology;

[0024] The data upload unit is connected to the cloud server via the ESP8266 Wi-Fi module to achieve real-time data upload;

[0025] The encryption unit is used to encrypt, segment, split the key, judge the data integrity and decrypt the data file in real time.

[0026] Preferably, the data transmission unit includes a link adaptive adjustment unit, a transmission mode switching unit, and a network architecture optimization unit;

[0027] The link adaptation adjustment unit is used to adapt to changes in channel quality based on link adaptation and related models in NB-IoT, while considering the selection of modulation and coding strategies and the number of repetitions;

[0028] The transmission mode switching unit is used to switch between the control plane transmission mode and the user plane transmission mode according to different requirements;

[0029] The network architecture optimization unit is used to perform independent deployment, protection band deployment or in-band deployment according to different network environments and business requirements.

[0030] Preferably, the cloud server includes a signal conversion unit, a preprocessing unit, a feature extraction unit, and an analysis and early warning unit;

[0031] The signal conversion unit is used to convert the analog signal output by the sensor into a digital signal to obtain multi-source parameter data;

[0032] The preprocessing unit is used to perform data fusion on the multi-source parameter data through a data fusion algorithm to obtain fused data; and then perform noise filtering and data cleaning on the fused data;

[0033] The feature extraction unit is used to extract features from the data processed by the preprocessing unit;

[0034] The analysis and early warning unit is used to input the extracted data features into the improved long short-term memory network model, analyze and warn of on-site safety risks, and obtain prediction results.

[0035] Preferably, the preprocessing unit includes a data synchronization processing subunit, a data fusion subunit, a data cleaning subunit, a data standardization subunit, and a data filtering subunit;

[0036] The data synchronization processing subunit is used to perform synchronization and alignment processing on sensor data with different sampling rates and viewing angles in time and space to obtain time series data;

[0037] The data fusion subunit is used to synthesize the associated probability distribution of each sensor through a multi-Bayesian estimation method, provide a final fusion value, and perform multi-source data fusion according to the final fusion value to obtain fused data;

[0038] The data cleaning subunit is used to remove sensor noise through mean filtering, replace missing values ​​in sensor data with specific values ​​through the nan_to_num function by interpolation, and then identify and remove outliers through the Z-score method, or identify and correct outliers;

[0039] The data standardization subunit is used to perform data normalization by using StandardScaler of the Scikit-learn library to convert the data into a form with a mean of 0 and a standard deviation of 1; and then perform data standardization to convert the data into a form with zero mean and unit variance;

[0040] The data filtering subunit is used to perform filtering based on a low-pass filter combined with an exponential smoothing method to remove high-frequency noise.

[0041] Preferably, the data fusion subunit includes a priori probability allocation subunit, a likelihood function determination subunit, a posterior probability calculation subunit, a fused posterior probability distribution subunit, and a fusion value calculation subunit;

[0042] The prior probability allocation subunit is used to allocate a prior probability distribution to each sensor, and the prior probability distribution is used to reflect the cognition of the target variable in the absence of observation data;

[0043] The likelihood function determination subunit is used to determine the likelihood function of each sensor, and the likelihood function is used to describe the probability distribution of the target variable under the condition of given observation data;

[0044] The posterior probability calculation subunit is used to calculate the posterior probability distribution by combining the prior probability distribution and the likelihood function using the Bayesian formula; the posterior probability distribution is used to reflect the cognition of each sensor on the target variable when the observation data is available;

[0045] The fusion posterior probability distribution subunit is used to synthesize the posterior probability distributions of all sensors into a joint posterior probability distribution, and the joint posterior probability distribution is the final data fusion result;

[0046] The fusion value calculation subunit is used to calculate the final fusion value according to the fused joint posterior probability distribution.

[0047] Preferably, the improved long short-term memory network model generates an improved long short-term memory network model by constructing an attention mechanism module and a residual module, and introducing the attention mechanism module and the residual module into the long short-term memory network.

[0048] Preferably, the mobile terminal further includes a user rights management module and an education and training module;

[0049] The user rights management module is used to implement strict user rights management to ensure that staff with different roles can only access corresponding information and functions;

[0050] The education and training module is used to push personalized safety education and training content based on the staff's position and historical performance.

[0051] Compared with the prior art, the present invention has the following advantages and technical effects:

[0052] The present invention improves the real-time and response speed of on-site management, makes decision-making faster and more accurate, enhances the efficiency of data sharing and collaborative work, and reduces the phenomenon of information islands. The convenience of mobile terminals improves the work efficiency and flexibility of on-site staff, and the intelligent safety monitoring system effectively improves the safety of on-site staff.

[0053] The present invention constructs an efficient, reliable and safe power engineering site management system to realize real-time data collection, transmission, processing and analysis, thereby improving the management efficiency and decision-making quality of power engineering, and further effectively improving the safety of power engineering sites, which has high practical value and market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0055] Figure 1 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0056] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] like Figure 1 As shown, in this embodiment, a power engineering site management system based on a mobile terminal is provided, including:

[0059] The data acquisition module is used to collect on-site engineering progress data, equipment status data, and environmental parameter data in real time through sensors and communication equipment deployed at the power engineering site;

[0060] A data transmission module, connected to the data acquisition module, for transmitting the collected data to the cloud server in real time via a mobile network;

[0061] The cloud server is connected to the data transmission module and is used to process the data after receiving it and generate an analysis report;

[0062] The mobile terminal is connected to the cloud server to feed back analysis reports to on-site staff, monitor on-site safety conditions in real time, and issue warnings immediately when abnormalities are found.

[0063] Further, the data acquisition module includes a first acquisition unit, a second acquisition unit, and a third acquisition unit;

[0064] The first acquisition unit is used to collect equipment status data at the power engineering site based on distributed sensors;

[0065] The second acquisition unit is used to obtain the surrounding environment data of the power engineering site;

[0066] The third collection unit is used to obtain project progress data through regular and continuous monitoring.

[0067] Further, the distributed sensors include, but are not limited to, current and voltage sensors, temperature sensors, vibration sensors, acoustic wave sensors, infrared imaging sensors, dissolved gas sensors in oil, distributed optical fiber sensors, partial discharge sensors, MEMS sensors, magnetoresistive current sensors, and ultra-high frequency sensors;

[0068] Among them, temperature sensors are used to monitor temperature changes during equipment operation, such as thermistors and thermocouples, to prevent failures caused by overheating.

[0069] Vibration sensor: It detects vibration signals from equipment such as transformers and analyzes changes in their mechanical properties to assess equipment status.

[0070] Acoustic wave sensor: Use a sound level meter to detect the vibration noise of transformers or reactors and the environmental noise of substations to help identify the source of equipment failure.

[0071] Infrared imaging sensor: Use infrared thermal imagers to obtain the surface temperature of the equipment and detect problems such as poor contact and insulation degradation.

[0072] Dissolved Gas Sensor in Oil: Used in oil-filled and oil-paper insulated equipment to evaluate equipment failure by analyzing the composition and concentration of dissolved gas in the oil.

[0073] Distributed fiber optic sensors: Distributed temperature monitoring using fiber optic sensors can reflect the temperature rise of key components of the equipment in real time.

[0074] Partial discharge sensor: used to detect partial discharge of power equipment and realize fault diagnosis through ultra-high frequency, ultrasonic or transient ground voltage sensor.

[0075] MEMS sensor: Micro-electromechanical system sensor, which has the advantages of small size and low power consumption, and can be used for non-contact voltage and current measurement.

[0076] Magnetoresistive current sensing technology: Magnetoresistive sensors are used to sense changes in magnetic fields and measure current, with high sensitivity and low power consumption.

[0077] Ultra-high frequency (UHF) sensor: used for detection and location of partial discharge, and performs fault diagnosis by receiving high-frequency electromagnetic waves generated by partial discharge.

[0078] The application of these sensors can effectively improve the monitoring capabilities of the power system, achieve early warning and precise positioning of faults, and thus ensure the safe and stable operation of the power grid.

[0079] The second acquisition unit includes at least a temperature and humidity sensor, an electrochemical sensor, and an underground pipeline data acquisition unit;

[0080] Temperature and humidity sensors are used to monitor the temperature and humidity of the environment surrounding the equipment at the power engineering site;

[0081] The underground pipeline data acquisition unit is used to conduct detailed detection of the location, direction, and buried depth of underground pipelines, and record the attribute information of the pipelines;

[0082] Electrochemical sensors are used to monitor specific gas environments by reacting with the gas in the measured medium and generating an electrical signal that is proportional to the gas concentration.

[0083] Furthermore, the data transmission module includes a positioning unit, a data transmission unit, a data uploading unit, and an encryption unit;

[0084] The positioning unit uses STM32 as the main control and combines GPS or Beidou satellite to obtain positioning information;

[0085] The data transmission unit is based on NB-IoT network technology and transmits data in a push-pull or push manner; the system network architecture is divided into a sensor layer, a transmission layer, a data aggregation layer, a data storage layer and an application layer. Through the collaborative work of these layers, remote management and visual command of the construction site can be achieved.

[0086] The data upload unit is connected to the cloud server through the ESP8266 Wi-Fi module to achieve real-time data upload;

[0087] The encryption unit is used to encrypt, segment, split the key, judge the data integrity and decrypt the data files in real time.

[0088] Further, the data transmission unit includes a link adaptive adjustment unit, a transmission mode switching unit, and a network architecture optimization unit;

[0089] The link adaptation adjustment unit is used to adapt to the change of channel quality according to the link adaptation and related models in NB-IoT, while considering the selection of modulation and coding strategy and repetition number;

[0090] More specifically, for dynamic scenarios where devices move at low speeds, the number of repetitions is adjusted based on the link status information to solve the problem of rapid changes in channel quality, and then the MCS and number of repetitions are dynamically adjusted based on confirmation information feedback to solve the problem of slow changes in channel quality.

[0091] The transmission mode switching unit is used to switch between the control plane transmission mode and the user plane transmission mode according to different requirements;

[0092] The network architecture optimization unit is used for independent deployment, protection band deployment or in-band deployment according to different network environments and business requirements.

[0093] The data collected by the sensor in this embodiment is transmitted and monitored in real time using the cloud service platform through the MQTT protocol to adapt to the growth of equipment scale and the requirements of real-time and stability. Through the above technical means and processes, data can be effectively collected from mobile terminals and transmitted to cloud servers in real time through mobile networks, meeting the requirements of data real-time, stability and security in the big data era.

[0094] Furthermore, the cloud server includes a signal conversion unit, a preprocessing unit, a feature extraction unit, and an analysis and early warning unit;

[0095] The signal conversion unit is used to convert the analog signal output by the sensor into a digital signal to obtain multi-source parameter data;

[0096] The preprocessing unit is used to fuse the multi-source parameter data through a data fusion algorithm to obtain fused data; then the fused data is subjected to noise filtering and data cleaning;

[0097] The feature extraction unit is used to extract features from the data processed by the preprocessing unit;

[0098] The analysis and early warning unit is used to input the extracted data features into the improved long short-term memory network model, analyze and warn of on-site safety risks, and obtain prediction results.

[0099] This embodiment uses the long short-term memory network model to analyze and judge the safety status of equipment operation in real time, promptly discover anomalies and potential safety hazards, and provide corresponding warning information and decision support.

[0100] Early warning includes triggering corresponding early warning mechanisms based on risk prediction results, such as sound and light alarms, notifying relevant staff via phone or text messages for processing, automatically stopping equipment operation, etc., in order to prevent and control safety risks.

[0101] More specifically, the early warning mechanism triggering process includes:

[0102] Threshold setting: According to the safety requirements of equipment operation and historical operation status data, set the early warning threshold of the risk level. When the risk level predicted by the model exceeds this threshold, the early warning mechanism will be triggered.

[0103] Real-time monitoring: The system monitors the risk level of the model output in real time. Once it detects that the risk level exceeds the warning threshold, the warning mechanism is immediately activated. Real-time monitoring can be achieved through scheduled tasks or event triggering to ensure the timeliness and accuracy of the warning mechanism.

[0104] Multi-level warning: Design a multi-level warning mechanism to trigger different levels of warnings according to different risk levels. For example, when the risk level is medium risk, a warning prompt can be triggered to remind the operator to pay attention; when the risk level is high risk, an emergency stop operation can be triggered to ensure the safety of personnel and equipment.

[0105] Warning information transmission: Warning information needs to be delivered to relevant personnel in a timely and accurate manner. This embodiment ensures that information can be received and understood in a timely manner through various means such as sound and light alarms, SMS notifications, and system pop-up windows. Warning information should include risk levels, possible impacts, and recommended response measures to help operators make correct decisions.

[0106] Emergency response: After the early warning mechanism is triggered, corresponding emergency response measures are required, including stopping equipment operation, self-checking equipment status, etc., to ensure the safety of personnel and equipment. Emergency response measures should formulate detailed operation procedures and division of responsibilities based on risk assessment results and early warning information.

[0107] Feedback mechanism: After the early warning mechanism is triggered, feedback information is collected to evaluate the accuracy of the early warning and the effectiveness of the emergency response. By using this feedback information to optimize the model and early warning mechanism, the early warning effect in the future can be improved. The feedback mechanism can be implemented through log records, user surveys or expert reviews to ensure its comprehensiveness and objectivity. At the same time, in order to improve the effectiveness of the risk assessment and early warning module, the system needs to be continuously optimized. This includes model updates, parameter adjustments, user interactions and other aspects. The model update of this embodiment can be achieved through retraining or incremental learning to adapt to new data distribution and changes in the equipment operating environment. Parameter adjustment optimizes the accuracy and timeliness of risk assessment based on the early warning effect and actual needs. User interaction allows operators to adjust the early warning parameters according to actual conditions, or provide feedback to help the system better adapt to different operating conditions by providing a user interface.

[0108] Further, the preprocessing unit includes a data synchronization processing subunit, a data fusion subunit, a data cleaning subunit, a data standardization subunit, and a data filtering subunit;

[0109] The data synchronization processing subunit is used to synchronize and align sensor data with different sampling rates and viewing angles in time and space to obtain time series data;

[0110] The data fusion subunit is used to synthesize the associated probability distribution of each sensor through the multi-Bayesian estimation method, provide the final fusion value, and perform multi-source data fusion according to the final fusion value to obtain fused data;

[0111] The data cleaning subunit is used to remove sensor noise through mean filtering. For missing values ​​in sensor data, the missing values ​​are replaced with specific values ​​through the nan_to_num function using interpolation, and then outliers are identified and removed or identified and corrected through the Z-score method.

[0112] The data standardization subunit is used to normalize the data by using the StandardScaler of the Scikit-learn library to convert the data into a form with a mean of 0 and a standard deviation of 1; then data standardization is performed to convert the data into a form with zero mean and unit variance;

[0113] The data filtering subunit is used to perform filtering based on a low-pass filter combined with an exponential smoothing method to remove high-frequency noise.

[0114] Further, the data fusion subunit includes a priori probability allocation subunit, a likelihood function determination subunit, a posterior probability calculation subunit, a fused posterior probability distribution subunit, and a fused value calculation subunit;

[0115] The prior probability assignment subunit is used to assign a prior probability distribution to each sensor, and the prior probability distribution is used to reflect the cognition of the target variable in the absence of observed data;

[0116] The likelihood function determination subunit is used to determine the likelihood function of each sensor, and the likelihood function is used to describe the probability distribution of the target variable under the condition of given observation data;

[0117] The posterior probability calculation subunit is used to calculate the posterior probability distribution by combining the prior probability distribution and the likelihood function using the Bayesian formula; the posterior probability distribution is used to reflect each sensor's cognition of the target variable when the observation data is available;

[0118] The fusion posterior probability distribution subunit is used to synthesize the posterior probability distributions of all sensors into a joint posterior probability distribution, which is the final data fusion result;

[0119] The fusion value calculation subunit is used to calculate the final fusion value according to the fused joint posterior probability distribution.

[0120] Furthermore, the improved long short-term memory network model generates an improved long short-term memory network model by constructing an attention mechanism module and a residual module, and introducing the attention mechanism module and the residual module into the long short-term memory network.

[0121] Specifically, after constructing the attention mechanism module and the residual module, the attention mechanism module is introduced into the first three layers of the hidden layer of the short-term memory network to obtain an attention classification model; then the residual module is introduced into the attention classification model to generate an improved long short-term memory network model.

[0122] Furthermore, the mobile terminal also includes a user rights management module and an education and training module;

[0123] The user rights management module is used to implement strict user rights management to ensure that staff with different roles can only access corresponding information and functions;

[0124] The education and training module is used to push personalized safety education and training content based on the staff's position and historical performance.

[0125] This embodiment uses various sensors to collect key parameters of power equipment in real time. These sensors convert the monitored analog signals into digital signals and transmit them to the data collection point via wired or wireless means. The collected data is transmitted to the cloud server platform, where data fusion and comprehensive analysis are performed. Through edge computing technology, some sensors also have data preprocessing capabilities to reduce the amount of transmitted data and provide rapid response.

[0126] The cloud server platform uses big data and artificial intelligence algorithm models to conduct in-depth analysis of the collected data to identify abnormal patterns and potential faults in equipment operation, thereby achieving fault prediction and health status assessment. The analysis results are used to monitor the operating status of the power system in real time and optimize the management of the power grid through intelligent control strategies. For example, if a current sensor in a certain area detects abnormal current, the system will automatically adjust the operating parameters of the power grid or notify the operation and maintenance personnel to conduct inspections.

[0127] The sensor network of this embodiment can respond to fault events in the power grid in a timely manner. For example, if an abnormal discharge phenomenon is detected by a partial discharge sensor, the system will quickly locate the fault location and notify relevant personnel through the early warning system to take measures to avoid the expansion of the fault. At the same time, the design of the power sensor takes into account different usage environments, such as temperature, humidity, sunlight radiation, wind, earthquake and other conditions, to ensure that the sensor can work stably in various environments.

[0128] The management system of this embodiment uses a collaborative working mechanism to enable the power system to achieve efficient and reliable operation while ensuring the safety and stability of the power grid.

[0129] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A power engineering site management system based on a mobile terminal, characterized in that: include: The data acquisition module is used to collect on-site engineering progress data, equipment status data, and environmental parameter data in real time through sensors and communication equipment deployed at the power engineering site; A data transmission module, connected to the data acquisition module, for transmitting the collected data to the cloud server in real time via a mobile network; A cloud server, connected to the data transmission module, is used to process the data after receiving it and generate an analysis report; The mobile terminal is connected to the cloud server and is used to feed back the analysis report to the on-site staff, monitor the on-site safety status in real time, and issue an early warning immediately after an abnormality is found.

2. The mobile terminal-based power engineering site management system according to claim 1, characterized in that: The data acquisition module includes a first acquisition unit, a second acquisition unit, and a third acquisition unit; The first acquisition unit is used to collect equipment status data at the power engineering site based on distributed sensors; The second acquisition unit is used to obtain the surrounding environment data of the power engineering site; The third acquisition unit is used to obtain engineering progress data through regular and continuous monitoring.

3. The mobile terminal-based power engineering site management system according to claim 2, characterized in that: The distributed sensors include but are not limited to current and voltage sensors, temperature sensors, vibration sensors, acoustic wave sensors, infrared imaging sensors, dissolved gas sensors in oil, distributed optical fiber sensors, partial discharge sensors, MEMS sensors, magnetoresistive current sensors, and ultra-high frequency sensors; The second acquisition unit includes at least a temperature and humidity sensor, an electrochemical sensor, and an underground pipeline data acquisition unit; The temperature and humidity sensor is used to monitor the temperature and humidity of the environment surrounding the equipment on the power engineering site; The electrochemical sensor is used to monitor a specific gas environment by reacting with the gas in the measured medium to generate an electrical signal proportional to the gas concentration; The underground pipeline data acquisition unit is used to conduct detailed detection of the location, direction, and buried depth of the underground pipeline and record the attribute information of the pipeline.

4. The mobile terminal-based power engineering site management system according to claim 1, characterized in that: The data transmission module includes a positioning unit, a data transmission unit, a data uploading unit, and an encryption unit; The positioning unit uses STM32 as the main control and combines GPS or Beidou satellite to obtain positioning information; The data transmission unit transmits data in a push-pull or push manner based on NB-IoT network technology; The data upload unit is connected to the cloud server via the ESP8266 Wi-Fi module to achieve real-time data upload; The encryption unit is used to encrypt, segment, split the key, judge the data integrity and decrypt the data file in real time.

5. The mobile terminal-based power engineering site management system according to claim 1, characterized in that: The data transmission unit includes a link adaptive adjustment unit, a transmission mode switching unit, and a network architecture optimization unit; The link adaptation adjustment unit is used to adapt to changes in channel quality based on link adaptation and related models in NB-IoT, while considering the selection of modulation and coding strategies and the number of repetitions; The transmission mode switching unit is used to switch between the control plane transmission mode and the user plane transmission mode according to different requirements; The network architecture optimization unit is used to perform independent deployment, protection band deployment or in-band deployment according to different network environments and business requirements.

6. The mobile terminal-based power engineering site management system according to claim 1, characterized in that: The cloud server includes a signal conversion unit, a preprocessing unit, a feature extraction unit, and an analysis and early warning unit; The signal conversion unit is used to convert the analog signal output by the sensor into a digital signal to obtain multi-source parameter data; The preprocessing unit is used to perform data fusion on the multi-source parameter data through a data fusion algorithm to obtain fused data; and then perform noise filtering and data cleaning on the fused data; The feature extraction unit is used to extract features from the data processed by the preprocessing unit; The analysis and early warning unit is used to input the extracted data features into the improved long short-term memory network model, analyze and warn of on-site safety risks, and obtain prediction results.

7. The mobile terminal-based power engineering site management system according to claim 6, characterized in that: The preprocessing unit includes a data synchronization processing subunit, a data fusion subunit, a data cleaning subunit, a data standardization subunit, and a data filtering subunit; The data synchronization processing subunit is used to perform synchronization and alignment processing on sensor data with different sampling rates and viewing angles in time and space to obtain time series data; The data fusion subunit is used to synthesize the associated probability distribution of each sensor through a multi-Bayesian estimation method, provide a final fusion value, and perform multi-source data fusion according to the final fusion value to obtain fused data; The data cleaning subunit is used to remove sensor noise through mean filtering, replace missing values ​​in sensor data with specific values ​​through the nan_to_num function by interpolation, and then identify and remove outliers through the Z-score method, or identify and correct outliers; The data standardization subunit is used to perform data normalization by using StandardScaler of the Scikit-learn library to convert the data into a form with a mean of 0 and a standard deviation of 1; and then perform data standardization to convert the data into a form with zero mean and unit variance; The data filtering subunit is used to perform filtering based on a low-pass filter combined with an exponential smoothing method to remove high-frequency noise.

8. The mobile terminal-based power engineering site management system according to claim 7, characterized in that: The data fusion subunit includes a priori probability allocation subunit, a likelihood function determination subunit, a posterior probability calculation subunit, a fused posterior probability distribution subunit, and a fusion value calculation subunit; The prior probability allocation subunit is used to allocate a prior probability distribution to each sensor, and the prior probability distribution is used to reflect the cognition of the target variable in the absence of observation data; The likelihood function determination subunit is used to determine the likelihood function of each sensor, and the likelihood function is used to describe the probability distribution of the target variable under the condition of given observation data; The posterior probability calculation subunit is used to calculate the posterior probability distribution by combining the prior probability distribution and the likelihood function using the Bayesian formula; the posterior probability distribution is used to reflect the cognition of each sensor on the target variable when the observation data is available; The fusion posterior probability distribution subunit is used to synthesize the posterior probability distributions of all sensors into a joint posterior probability distribution, and the joint posterior probability distribution is the final data fusion result; The fusion value calculation subunit is used to calculate the final fusion value according to the fused joint posterior probability distribution.

9. The mobile terminal-based power engineering site management system according to claim 6, characterized in that: The improved long short-term memory network model generates an improved long short-term memory network model by constructing an attention mechanism module and a residual module, and introducing the attention mechanism module and the residual module into the long short-term memory network.

10. The mobile terminal-based power engineering site management system according to claim 1, characterized in that: The mobile terminal also includes a user rights management module and an education and training module; The user rights management module is used to implement strict user rights management to ensure that staff with different roles can only access corresponding information and functions; The education and training module is used to push personalized safety education and training content based on the staff's position and historical performance.

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