Power construction risk intelligent early warning method and device based on multi-dimensional information fusion

Abstract

The invention provides an electric power construction risk intelligent early warning method and device based on multi-dimensional information fusion, and relates to the technical field of electric power construction risk intelligent early warning. Monitoring environment change information, equipment operation state information and safety distance information of the power construction site; determining a first early warning result based on the environment change information and a first judgment rule; determining a second early warning result based on the equipment operation state information and a second judgment rule; determining a third early warning result based on the safety distance information and a third judgment rule; and based on the first early warning result, the second early warning result and the third early warning result, determining a power construction risk level. Therefore, potential safety hazards are found by monitoring the environment change, the equipment operation state and the safety distance in real time, so that the occurrence of construction accidents is reduced. Therefore, the life safety of constructors can be protected, and the property loss can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent early warning of electric power construction risks, and in particular to an intelligent early warning method and device for electric power construction risks based on multi-dimensional information fusion. Background Art

[0002] In the field of power construction, as the scale of power grids continues to expand and the complexity increases, the safety risk issues in the construction process are becoming increasingly prominent. Traditional risk management methods often rely on manual inspections and empirical judgments. This method is not only inefficient, but also difficult to fully and real-time capture the multi-dimensional risk information of the construction site, which can easily lead to underreporting or false reporting of safety hazards, thereby threatening the personal safety of construction workers, equipment safety and the stable operation of the power grid.

[0003] In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, new solutions have been provided for early warning of power construction risks. By deploying various sensors, real-time monitoring of key parameters such as the construction site environment, equipment status, and safety distance can be achieved, providing a rich data source for risk early warning. However, single-dimensional monitoring data can often only reflect the risk status of a certain aspect, and it is difficult to fully assess the overall risk level during the construction process. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] The first embodiment of the present disclosure proposes an intelligent early warning method for power construction risks based on multi-dimensional information fusion, including: Based on environmental perception sensors, equipment status monitoring sensors and near-electricity safety sensors, the environmental change information, equipment operation status information and safety distance information of the power construction site are monitored; Determining a first warning result based on the environmental change information and a first determination rule; Determine a second warning result based on the equipment operation status information and the second determination rule; Determining a third warning result based on the safety distance information and the third determination rule; The risk level of the power construction is determined based on the first warning result, the second warning result and the third warning result.

[0006] The second embodiment of the present disclosure proposes an intelligent early warning device for electric power construction risks based on multi-dimensional information fusion, comprising: A monitoring module is used to monitor environmental change information, equipment operation status information and safety distance information at the power construction site based on environmental perception sensors, equipment status monitoring sensors and near-electricity safety sensors; A first determination module, used to determine a first warning result based on the environmental change information and a first determination rule; A second determination module, used to determine a second warning result based on the equipment operation status information and a second determination rule; A third determination module, used to determine a third warning result based on the safety distance information and a third determination rule; A determination module is used to determine the risk level of power construction based on the first warning result, the second warning result and the third warning result.

[0007] The third aspect embodiment of the present disclosure proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the intelligent early warning method for power construction risks based on multi-dimensional information fusion as proposed in the first aspect embodiment of the present disclosure.

[0008] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium storing a computer program, which, when executed by a processor, implements the intelligent early warning method for power construction risks based on multi-dimensional information fusion as proposed in the first aspect embodiment of the present disclosure.

[0009] The intelligent early warning method and device for electric power construction risk based on multi-dimensional information fusion provided by the present disclosure have the following beneficial effects: In the disclosed embodiment, firstly, the environmental change information, equipment operation status information and safety distance information of the power construction site are monitored based on the environmental perception sensor, the equipment status monitoring sensor and the near-electric safety sensor respectively, and then the first warning result is determined based on the environmental change information and the first determination rule, and then the second warning result is determined based on the equipment operation status information and the second determination rule, and then the third warning result is determined based on the safety distance information and the third determination rule, and finally the risk level of the power construction is determined based on the first warning result, the second warning result and the third warning result. Thus, by real-time monitoring of environmental changes, equipment operation status and safety distance, potential safety hazards can be discovered in time, thereby avoiding or reducing the occurrence of construction accidents. This helps to protect the lives of construction personnel and reduce property losses. The first, second and third determination rules are set, and the warning results are determined based on these rules, respectively, so that the risk warning is more accurate and timely. This multi-level and multi-dimensional warning mechanism can cover various risk points in the construction process more comprehensively. Based on the comprehensive judgment of the three warning results, the risk level of the power construction can be accurately assessed. This provides a scientific decision-making basis for construction management personnel, which helps them formulate corresponding countermeasures and management strategies according to the risk level. By promptly identifying and addressing potential safety hazards, construction interruptions and delays caused by accidents can be avoided. This helps improve construction efficiency and ensures that the project proceeds smoothly as planned. By collecting and analyzing large amounts of real-time data, more accurate risk assessment models can be established. These models can predict future risk trends based on historical data and current conditions, providing more powerful data support for construction management.

[0010] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flow chart of an intelligent early warning method for electric power construction risk based on multi-dimensional information fusion provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of an intelligent early warning device for electric power construction risks based on multi-dimensional information fusion provided by an embodiment of the present disclosure; Figure 3 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0012] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0013] The following describes, with reference to the accompanying drawings, an intelligent early warning method, system, computer equipment and storage medium for electric power construction risks based on multi-dimensional information fusion according to an embodiment of the present disclosure.

[0014] It should be noted that the executor of the method for intelligent early warning of power construction risks based on multi-dimensional information fusion in the embodiment of the present disclosure is an intelligent early warning device for power construction risks based on multi-dimensional information fusion. The system can be implemented by software and / or hardware, and the system can be configured in any electronic device.

[0015] Figure 1 A flow chart of an intelligent early warning method for power construction risks based on multi-dimensional information fusion provided in the first embodiment of the present disclosure.

[0016] like Figure 1 As shown, the intelligent early warning method for power construction risks based on multi-dimensional information fusion may include the following steps: Step 101 , monitoring environmental change information, equipment operation status information and safety distance information of the power construction site based on environmental perception sensors, equipment status monitoring sensors and near-electricity safety sensors respectively.

[0017] It should be noted that at power construction sites, in order to comprehensively monitor and warn of potential safety risks, three key sensor systems are usually deployed: environmental perception sensors, equipment status monitoring sensors, and near-electricity safety sensors. These sensors each have the task of monitoring information in different dimensions, and together provide real-time and accurate data support for construction risk management.

[0018] Environmental perception sensors are mainly used to monitor environmental changes at power construction sites, including but not limited to the following aspects: Temperature and humidity sensors: monitor temperature and humidity changes at the construction site to assess whether environmental conditions are suitable for construction workers' work and the normal operation of equipment. Extreme temperature and humidity conditions may affect work efficiency and equipment performance. Wind speed and direction sensors: measure wind speed and direction, especially when working at high altitudes or building temporary structures, strong winds may cause safety hazards. Precipitation sensors: monitor rainfall conditions to avoid outdoor work in bad weather and reduce the risks of electric shock, slipping, etc. Light intensity sensors: monitor light intensity in work areas where light conditions need to be precisely controlled (such as welding, precision equipment installation, etc.) to ensure work quality and safety.

[0019] Equipment status monitoring sensors focus on collecting operating status information of various equipment at power construction sites in order to promptly detect and handle potential faults or abnormal conditions. These sensors typically include: Vibration sensors: installed on rotating machinery (such as generators and motors) to monitor their vibration frequency and amplitude to assess the health of the equipment. Temperature sensors: measure the temperature of key components to prevent equipment damage or fire risks caused by overheating. Current and voltage sensors: monitor the current and voltage of electrical equipment to ensure that it operates within a safe and stable range. Oil level and oil quality sensors (for oil-containing equipment): monitor the oil level and oil quality of lubricating oil or insulating oil to prevent equipment failures due to insufficient or contaminated oil.

[0020] Near-electric safety sensors are an important tool for ensuring a safe distance between construction workers and live equipment. They combine multiple technologies to ensure the safety of construction workers: Electric field strength sensors: Detect the electric field strength in the surrounding environment, especially when working near high-voltage lines, to help construction workers determine the safe distance from live objects. Distance sensors: Usually used in conjunction with electric field strength sensors to directly measure the physical distance between construction workers and live equipment, and issue warnings when approaching dangerous thresholds. GPS positioning technology (indirect participation): Although not directly used as a near-electric safety sensor, GPS positioning technology can assist in determining the specific location of construction workers, combined with the known location of live equipment, evaluate the safe distance through software algorithms, and trigger early warnings when necessary.

[0021] In summary, these three types of sensors, through their unique monitoring capabilities, together constitute the core of the risk early warning system for power construction sites, providing comprehensive and real-time information support for construction management personnel, and effectively improving construction safety and efficiency.

[0022] Step 102: Determine a first warning result based on the environmental change information and the first determination rule.

[0023] Optionally, the environmental perception sensor includes at least a temperature and humidity sensor, an anemometer and a gas detector, and the environmental change information includes at least temperature, humidity, wind speed, wind direction and concentration of harmful gases.

[0024] Specifically, the collected temperature, humidity, wind speed, wind direction and harmful gas concentration can be compared with the corresponding preset safety range thresholds to obtain a comparison result. Based on the comparison result, it is judged whether the current environment of the power construction site is suitable for construction to obtain a first warning result.

[0025] Among them, if the current environment of the power construction site is suitable for construction, the first warning result is that the construction is safe, otherwise there are risks in the construction.

[0026] It should be noted that environmental perception sensors are very important at power construction sites. They provide key data support for construction safety management by monitoring various environmental parameters at the construction site in real time.

[0027] Among them, temperature and humidity sensors are used to monitor the temperature and humidity of the construction site. These two parameters directly affect the comfort and work efficiency of construction workers, and also have an impact on the performance of some construction materials and equipment. Anemometers are used to measure wind speed and direction, which is particularly important for high-risk operations such as high-altitude operations and building temporary structures. Strong winds may cause unstable operations and even cause safety accidents. Gas detectors are used to detect the concentration of harmful gases in the air at the construction site, such as methane and carbon monoxide, to ensure that construction workers work in a non-toxic environment.

[0028] Among them, the temperature can reflect the degree of hotness or coldness of the construction site. Too high or too low temperature may have an adverse effect on the construction. Among them, humidity affects the storage and use of materials. High humidity may cause the materials to become damp and deteriorate, and low humidity may cause problems such as static electricity. Among them, wind speed can evaluate the impact of wind on operations, especially when working at high altitudes and building temporary structures. Among them, in some cases, wind direction may affect the diffusion direction of harmful gases, posing a potential threat to construction workers. Among them, for the concentration of harmful gases, it is necessary to ensure the air quality of the construction site to prevent harmful gases from exceeding the standard and causing poisoning accidents.

[0029] Specifically, the temperature, humidity, wind speed, wind direction and harmful gas concentration data collected by the temperature and humidity sensors, anemometers and gas detectors can be compared with the preset safety range thresholds.

[0030] These thresholds are usually determined based on industry standards, historical data analysis and expert experience to ensure the safety and suitability of the construction environment. Based on the comparison results, if any environmental parameter exceeds the preset safety range threshold, the current power construction site environment is judged to be unsuitable for construction.

[0031] As an exemplary scenario, a power construction team is carrying out the construction of a high-voltage transmission line. The construction site is located in an open area, but the recent weather forecast shows that there may be gusty weather, and there are potential industrial emission sources near the construction area. Temperature and humidity sensors can be deployed at multiple key locations on the construction site, such as workers' rest areas, material storage areas, and work areas to monitor the temperature and humidity of the construction environment. Anemometers can be installed at high locations on the construction site, such as tower cranes or temporary observation towers, to measure wind speed and direction in real time. Gas detectors can be arranged in the downwind direction that may be affected by industrial emissions, as well as at the boundaries of the construction area to monitor the concentration of harmful gases.

[0032] For example, suppose the temperature at the current construction site is 35°C and the humidity is 70%. The preset safety range thresholds are that the temperature does not exceed 30°C and the humidity does not exceed 60%. The comparison result is that both the temperature and humidity exceed the preset safety range thresholds. The real-time wind speed measurement result is 8 meters per second, and the preset safety wind speed threshold is no more than 6 meters per second (taking into account the high-altitude operation and the stability of the temporary structure). The comparison result is that the wind speed exceeds the preset safety range threshold. For example, the gas detector shows that the sulfur dioxide concentration is 0.05mg / m³, and the preset safety concentration threshold is no more than 0.01mg / m³. The comparison result is that the concentration of harmful gases exceeds the preset safety range threshold.

[0033] Specifically, based on the above comparison results, it can be judged that the current environment of the power construction site is not suitable for continued construction: high temperature and high humidity may cause heatstroke and dehydration to construction workers, and affect the performance of construction materials. Wind speeds beyond the safe range may increase the risk of high-altitude operations, threaten the safety of construction workers, and affect the stability of temporary structures. Excessive concentrations of harmful gases may cause harm to the health of construction workers.

[0034] Step 103: Determine a second warning result based on the equipment operation status information and the second determination rule.

[0035] Optionally, the equipment status monitoring sensor includes at least a vibration sensor, an equipment temperature sensor, and a current and voltage sensor, and the equipment operation status information includes at least the vibration condition, temperature, current, and voltage of the power construction equipment. The second early warning result is determined based on the equipment operation status information and the second determination rule, including: A second warning result is determined based on the equipment operation status information and a corresponding trend analysis strategy and / or a fault diagnosis model.

[0036] It should be noted that at power construction sites, equipment status monitoring sensors are essential to ensure the safe operation of construction equipment. These sensors can collect real-time information on the operating status of equipment and predict potential failures or abnormal conditions through trend analysis strategies and / or fault diagnosis models, thereby triggering early warnings in a timely manner.

[0037] Among them, vibration sensors are installed on rotating parts or key positions of power construction equipment to monitor the vibration of the equipment. Abnormal vibration usually indicates problems such as imbalance, wear or looseness inside the equipment. Equipment temperature sensors measure the temperature of key components of the equipment, such as motors, transformers, etc. Excessive temperature may indicate equipment overload, cooling system failure or insulation aging. Current and voltage sensors monitor the current and voltage of the equipment during operation to ensure that they fluctuate within the rated range. Abnormal current and voltage may indicate circuit failure, load imbalance or equipment performance degradation. The data collected by the vibration sensor can be used to analyze parameters such as the vibration frequency, amplitude and phase of the equipment to evaluate the operating status of the equipment. The equipment temperature sensor provides real-time temperature information of key components for monitoring the thermal state of the equipment. The current and voltage sensors record the current and voltage values ​​of the equipment during operation, reflecting the electrical performance of the equipment.

[0038] In order to determine the second warning result, it is necessary to use a trend analysis strategy and / or a fault diagnosis model to process the collected equipment operation status information.

[0039] Among them, the trend analysis strategy can be to conduct statistical analysis on historical data to identify the normal fluctuation range of the equipment operation status parameters. Monitor the current data in real time and compare it with the historical data to analyze the change trend of the parameters. If it is found that the parameters deviate from the normal fluctuation range or show abnormal change trends (such as sharp rise, fall or periodic fluctuations), it may indicate that the equipment is about to fail.

[0040] Among them, the fault diagnosis model can use machine learning, deep learning and other algorithms to establish a fault diagnosis model based on the equipment operation status information. The real-time collected data is input into the model, and the model makes inferences and judgments based on the preset algorithms and rules. If the model output indicates that the equipment has a fault or potential fault risk (such as identification of specific fault modes, prediction of fault probability, etc.), an early warning is triggered.

[0041] As an example, if a power construction site is conducting daily operation monitoring on a key transformer, vibration sensors, equipment temperature sensors, and current and voltage sensors are installed on site to ensure the safe operation of the transformer.

[0042] Vibration sensors are installed on the core components of the transformer to monitor its vibration in real time. The vibration data reflects the stability of the transformer's internal structure and the smoothness of its operating status. Equipment temperature sensors are arranged in key heat dissipation areas of the transformer, such as near the oil tank and windings, to monitor their temperature changes. Temperature data is an important indicator for evaluating the thermal load of the transformer and the efficiency of the cooling system. Current and voltage sensors are installed at the input and output ends of the transformer to measure the current and voltage values ​​in real time. These data reflect the electrical performance and load conditions of the transformer.

[0043] Specifically, vibration, temperature, current and voltage data can be transmitted to the central monitoring platform through sensors via wired or wireless means. Big data analysis technology is used to perform statistical analysis on historical data to establish the normal operating range (i.e., threshold) of the transformer. At the same time, the current data is monitored in real time and compared with historical data to analyze the trend of parameter changes. A fault diagnosis model is established based on a machine learning algorithm. After inputting real-time data, the model automatically identifies potential fault modes and predicts the probability of fault occurrence.

[0044] Suppose a vibration sensor detects a sudden increase in the vibration amplitude of a component inside a transformer, which is beyond the normal range. This may indicate that the component is worn, loose, or unbalanced.

[0045] At the same time, the equipment temperature sensor shows that the temperature of the transformer tank continues to rise, approaching or exceeding the safety temperature threshold. This indicates that the transformer's cooling system may not be able to dissipate heat effectively, or there is local overheating inside.

[0046] The current and voltage sensors detect that the current value of the transformer fluctuates greatly and the voltage value is unstable. This may be related to load changes, circuit faults, or insulation problems inside the transformer.

[0047] Based on the above data, combined with trend analysis strategy and fault diagnosis model, the system makes a comprehensive judgment and believes that the transformer has serious operating abnormalities and potential fault risks.

[0048] Step 104, determining a third warning result based on the safety distance information and the third determination rule.

[0049] It should be noted that the near-electric safety sensor combines electric field strength detection, distance detection and GPS positioning technology to ensure that construction workers always maintain a safe distance from live equipment.

[0050] The electric field strength sensor is installed on the equipment worn by construction workers (such as safety helmets, work clothes, etc.) to detect the electric field strength of the surrounding environment in real time. The electric field strength sensor can sense the electric field generated by the live equipment and convert it into a measurable electrical signal. By analyzing the changes in the electric field strength, it is possible to preliminarily determine whether the distance between the construction workers and the live equipment is safe.

[0051] Distance sensors are used in conjunction with electric field strength sensors to directly measure the physical distance between construction workers and live equipment. Distance sensors can use technologies such as ultrasound, infrared or laser to achieve non-contact measurement. Real-time feedback of distance information is essential for accurate control of safety distances.

[0052] GPS positioning technology: integrated into the equipment worn by construction workers or handheld devices, it is used to determine the precise location of construction workers. Combined with the map data of the construction site and the known location of the live equipment, GPS positioning technology can calculate the relative position relationship between the construction workers and the live equipment.

[0053] When construction workers enter the power construction site, the near-electric safety sensor starts working. The electric field strength sensor detects the electric field strength of the surrounding environment in real time and transmits the data to the data processing center. The distance sensor also measures the physical distance between the construction workers and the live equipment and transmits the data synchronously. GPS positioning technology provides the construction workers with precise location information and compares it with the known location of the live equipment. The data processing center receives data from the electric field strength sensor, distance sensor and GPS positioning technology, and conducts comprehensive analysis and processing. Based on the preset safety distance threshold and electric field strength safety standard, the data processing center determines whether the construction workers are currently in a safe state. If it is found that the distance between the construction workers and the live equipment is less than the safety distance threshold, or the electric field strength exceeds the safety standard, the early warning mechanism is immediately triggered.

[0054] Optionally, if the distance between the construction personnel and the electric power construction equipment is less than the safety distance, the third warning result is determined as the presence of a risk of working near electricity; otherwise, there is no risk of working near electricity.

[0055] If the real-time distance is less than the safety distance threshold, the third warning result is determined to be "there is a risk of working near electricity". At this time, the system should immediately issue an audible and visual alarm and notify the construction personnel and on-site management personnel through wireless communication equipment. If the real-time distance is greater than or equal to the safety distance threshold, the third warning result is determined to be "there is no risk of working near electricity". At this time, the system can continue to maintain the monitoring state without issuing a warning signal.

[0056] Step 105: Determine the risk level of the power construction based on the first warning result, the second warning result and the third warning result.

[0057] Specifically, when the first warning result, the second warning result and the third warning result all indicate that the construction is safe, the risk level of the power construction is determined to be no risk; In the case that only one of the first warning result, the second warning result and the third warning result indicates that the construction is safe, determining that the risk level of the power construction is medium risk; In the case that only two of the first warning result, the second warning result and the third warning result indicate that the construction is safe, determining that the risk level of the power construction is low risk; When the first warning result, the second warning result and the third warning result all indicate that there is a construction risk, the power construction risk level is determined to be high risk.

[0058] Among them, for the risk-free level, when the first warning result, the second warning result and the third warning result all indicate that the construction is safe, that is, no warning system issues a risk warning, it can be judged that the risk level of the current power construction project is risk-free. This means that all key safety monitoring points are within the normal range and construction can be carried out safely.

[0059] If the first, second and third warning results all indicate construction risks, that is, all warning systems have issued risk warnings, then the risk level of the current power construction project should be assessed as high risk. This means that there are multiple serious safety hazards, and immediate measures must be taken to rectify and intervene to prevent accidents.

[0060] When only one of the three early warning results indicates construction safety, and the other two indicate construction risks, the risk level should be judged as medium risk. This means that although the safety status of one aspect is good, the risks of the other two aspects are still high, which requires sufficient attention and appropriate preventive measures.

[0061] Among the three early warning results, if only two indicate construction safety and the other indicates construction risk, the risk level should be assessed as low risk. This means that most of the safety monitoring points are in normal condition, but attention should still be paid to the aspect that issues the risk warning to prevent the risk from expanding or turning into a more serious risk.

[0062] In the disclosed embodiment, firstly, the environmental change information, equipment operation status information and safety distance information of the power construction site are monitored based on the environmental perception sensor, the equipment status monitoring sensor and the near-electric safety sensor respectively, and then the first warning result is determined based on the environmental change information and the first determination rule, and then the second warning result is determined based on the equipment operation status information and the second determination rule, and then the third warning result is determined based on the safety distance information and the third determination rule, and finally the risk level of the power construction is determined based on the first warning result, the second warning result and the third warning result. Thus, by real-time monitoring of environmental changes, equipment operation status and safety distance, potential safety hazards can be discovered in time, thereby avoiding or reducing the occurrence of construction accidents. This helps to protect the lives of construction personnel and reduce property losses. The first, second and third determination rules are set, and the warning results are determined based on these rules, respectively, so that the risk warning is more accurate and timely. This multi-level and multi-dimensional warning mechanism can cover various risk points in the construction process more comprehensively. Based on the comprehensive judgment of the three warning results, the risk level of the power construction can be accurately assessed. This provides a scientific decision-making basis for construction management personnel, which helps them formulate corresponding countermeasures and management strategies according to the risk level. By promptly identifying and addressing potential safety hazards, construction interruptions and delays caused by accidents can be avoided. This helps improve construction efficiency and ensures that the project proceeds smoothly as planned. By collecting and analyzing large amounts of real-time data, more accurate risk assessment models can be established. These models can predict future risk trends based on historical data and current conditions, providing more powerful data support for construction management.

[0063] In order to implement the above embodiments, the present disclosure also proposes an intelligent early warning device for power construction risks based on multi-dimensional information fusion.

[0064] Figure 2 This is a structural block diagram of an intelligent early warning device for power construction risks based on multi-dimensional information fusion provided in the second embodiment of the present disclosure.

[0065] like Figure 2 As shown, the intelligent early warning device 200 for electric power construction risk based on multi-dimensional information fusion may include: Monitoring module 210, used to monitor environmental change information, equipment operation status information and safety distance information of the power construction site based on environmental perception sensors, equipment status monitoring sensors and near-electricity safety sensors respectively; A first determination module 220, configured to determine a first warning result based on the environmental change information and a first determination rule; A second determination module 230, configured to determine a second warning result based on the device operation status information and a second determination rule; A third determination module 240, configured to determine a third warning result based on the safety distance information and a third determination rule; The determination module 250 is used to determine the risk level of the power construction based on the first warning result, the second warning result and the third warning result.

[0066] Optionally, the environment perception sensor includes at least a temperature and humidity sensor, an anemometer and a gas detector, the environment change information includes at least temperature, humidity, wind speed, wind direction and concentration of harmful gases, and the first determination module is specifically used to: Compare the collected temperature, humidity, wind speed, wind direction and harmful gas concentration with the corresponding preset safety range thresholds to obtain a comparison result; According to the comparison result, it is determined whether the current environment of the power construction site is suitable for construction to obtain a first warning result.

[0067] Optionally, the equipment status monitoring sensor includes at least a vibration sensor, an equipment temperature sensor and a current and voltage sensor, and the equipment operation status information includes at least the vibration condition, temperature, current and voltage of the power construction equipment. The second determination module is specifically used to: A second warning result is determined based on the equipment operation status information and a corresponding trend analysis strategy and / or a fault diagnosis model.

[0068] Optionally, the near-electric safety sensor includes at least: an electric field strength sensor and a distance sensor, and the third determination module is specifically used to: If the distance between the construction personnel and the electric power construction equipment is less than the safety distance, the third warning result is determined as the presence of a risk of working near electricity; otherwise, there is no risk of working near electricity.

[0069] Optionally, the determination module is specifically used to: When the first warning result, the second warning result and the third warning result all indicate that the construction is safe, determining the risk level of the power construction as no risk; In the case that only one of the first warning result, the second warning result and the third warning result indicates that the construction is safe, determining that the risk level of the power construction is medium risk; In the case that only two of the first warning result, the second warning result and the third warning result indicate that the construction is safe, determining that the risk level of the power construction is low risk; When the first warning result, the second warning result and the third warning result all indicate that there is a construction risk, the power construction risk level is determined to be high risk.

[0070] In the disclosed embodiment, firstly, the environmental change information, equipment operation status information and safety distance information of the power construction site are monitored based on the environmental perception sensor, the equipment status monitoring sensor and the near-electric safety sensor respectively, and then the first warning result is determined based on the environmental change information and the first determination rule, and then the second warning result is determined based on the equipment operation status information and the second determination rule, and then the third warning result is determined based on the safety distance information and the third determination rule, and finally the risk level of the power construction is determined based on the first warning result, the second warning result and the third warning result. Thus, by real-time monitoring of environmental changes, equipment operation status and safety distance, potential safety hazards can be discovered in time, thereby avoiding or reducing the occurrence of construction accidents. This helps to protect the lives of construction personnel and reduce property losses. The first, second and third determination rules are set, and the warning results are determined based on these rules, respectively, so that the risk warning is more accurate and timely. This multi-level and multi-dimensional warning mechanism can cover various risk points in the construction process more comprehensively. Based on the comprehensive judgment of the three warning results, the risk level of the power construction can be accurately assessed. This provides a scientific decision-making basis for construction management personnel, which helps them formulate corresponding countermeasures and management strategies according to the risk level. By promptly identifying and addressing potential safety hazards, construction interruptions and delays caused by accidents can be avoided. This helps improve construction efficiency and ensures that the project proceeds smoothly as planned. By collecting and analyzing large amounts of real-time data, more accurate risk assessment models can be established. These models can predict future risk trends based on historical data and current conditions, providing more powerful data support for construction management.

[0071] In order to implement the above embodiments, the present disclosure also proposes a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the intelligent early warning method for power construction risks based on multi-dimensional information fusion proposed in the above embodiments of the present disclosure is implemented.

[0072] In order to implement the above embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the intelligent early warning method for power construction risks based on multi-dimensional information fusion as proposed in the above embodiments of the present disclosure.

[0073] In order to implement the above embodiments, the present disclosure also proposes a computer program product. When the instruction processor in the computer program product is executed, it executes the intelligent early warning method for power construction risks based on multi-dimensional information fusion proposed in the above embodiments of the present disclosure.

[0074] Figure 3 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 3The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0075] like Figure 3 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0076] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (hereinafter referred to as: ISA) bus, Micro Channel Architecture (hereinafter referred to as: MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (hereinafter referred to as: VESA) local bus and Peripheral Component Interconnection (hereinafter referred to as: PCI) bus.

[0077] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0078] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, referred to as the "hard drive"). Although Figure 3Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a compact disc read only memory (hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive can be connected to the bus 18 through one or more data medium interfaces. The memory 28 may include at least one program product, which has a set (for example, at least one) of program modules, which are configured to perform the functions of the various embodiments of the present disclosure.

[0079] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 42 perform the functions and / or methods of the embodiments described in the present disclosure.

[0080] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0081] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the above embodiments.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0083] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0084] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0086] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0087] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0088] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0089] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. An intelligent early warning method for power construction risks based on multi-dimensional information fusion, characterized in that: include: Based on environmental perception sensors, equipment status monitoring sensors and near-electricity safety sensors, the environmental change information, equipment operation status information and safety distance information of the power construction site are monitored; Determining a first warning result based on the environmental change information and a first determination rule; Determine a second warning result based on the equipment operation status information and the second determination rule; Determining a third warning result based on the safety distance information and the third determination rule; The risk level of the power construction is determined based on the first warning result, the second warning result and the third warning result.

2. The method according to claim 1, characterized in that The environmental perception sensor includes at least a temperature and humidity sensor, an anemometer and a gas detector, the environmental change information includes at least temperature, humidity, wind speed, wind direction and concentration of harmful gases, and the first warning result is determined based on the environmental change information and the first determination rule, including: Compare the collected temperature, humidity, wind speed, wind direction and harmful gas concentration with the corresponding preset safety range thresholds to obtain a comparison result; According to the comparison result, it is determined whether the current environment of the power construction site is suitable for construction to obtain a first warning result.

3. The method according to claim 1, characterized in that The equipment status monitoring sensor includes at least a vibration sensor, an equipment temperature sensor and a current and voltage sensor, the equipment operation status information includes at least the vibration condition, temperature, current and voltage of the power construction equipment, and the second warning result is determined based on the equipment operation status information and the second determination rule, including: A second warning result is determined based on the equipment operation status information and a corresponding trend analysis strategy and / or a fault diagnosis model.

4. The method according to claim 1, characterized in that The near-electric safety sensor includes at least: an electric field strength sensor and a distance sensor, and the determining of the third warning result based on the safety distance information and the third determination rule includes: If the distance between the construction personnel and the electric power construction equipment is less than the safety distance, the third warning result is determined as the presence of a risk of working near electricity; otherwise, there is no risk of working near electricity.

5. The method according to claim 1, characterized in that The step of determining the risk level of power construction based on the first warning result, the second warning result and the third warning result includes: When the first warning result, the second warning result and the third warning result all indicate that the construction is safe, determining the risk level of the power construction as no risk; In the case that only one of the first warning result, the second warning result and the third warning result indicates that the construction is safe, determining that the risk level of the power construction is medium risk; In the case that only two of the first warning result, the second warning result and the third warning result indicate that the construction is safe, determining that the risk level of the power construction is low risk; When the first warning result, the second warning result and the third warning result all indicate that there is a construction risk, the power construction risk level is determined to be high risk.

6. An intelligent early warning device for power construction risks based on multi-dimensional information fusion, characterized in that: include: A monitoring module is used to monitor environmental change information, equipment operation status information and safety distance information at the power construction site based on environmental perception sensors, equipment status monitoring sensors and near-electricity safety sensors; A first determination module, used to determine a first warning result based on the environmental change information and a first determination rule; A second determination module, used to determine a second warning result based on the equipment operation status information and a second determination rule; A third determination module, used to determine a third warning result based on the safety distance information and a third determination rule; A determination module is used to determine the risk level of power construction based on the first warning result, the second warning result and the third warning result.

7. The device according to claim 6, characterized in that The environmental perception sensor includes at least a temperature and humidity sensor, an anemometer and a gas detector, the environmental change information includes at least temperature, humidity, wind speed, wind direction and concentration of harmful gases, and the first determination module is specifically used to: Compare the collected temperature, humidity, wind speed, wind direction and harmful gas concentration with the corresponding preset safety range thresholds to obtain a comparison result; According to the comparison result, it is determined whether the current environment of the power construction site is suitable for construction to obtain a first warning result.

8. The device according to claim 6, characterized in that The equipment status monitoring sensor includes at least a vibration sensor, an equipment temperature sensor and a current and voltage sensor, and the equipment operation status information includes at least the vibration condition, temperature, current and voltage of the power construction equipment. The second determination module is specifically used to: A second warning result is determined based on the equipment operation status information and a corresponding trend analysis strategy and / or a fault diagnosis model.

9. The device according to claim 6, characterized in that The near-electric safety sensor at least includes: an electric field strength sensor and a distance sensor, and the third determination module is specifically used to: If the distance between the construction personnel and the electric power construction equipment is less than the safety distance, the third warning result is determined as the presence of a risk of working near electricity; otherwise, there is no risk of working near electricity.

10. The device according to claim 6, characterized in that The determination module is specifically used for: When the first warning result, the second warning result and the third warning result all indicate that the construction is safe, determining the risk level of the power construction as no risk; In the case that only one of the first warning result, the second warning result and the third warning result indicates that the construction is safe, determining that the risk level of the power construction is medium risk; In the case that only two of the first warning result, the second warning result and the third warning result indicate that the construction is safe, determining that the risk level of the power construction is low risk; When the first warning result, the second warning result and the third warning result all indicate that there is a construction risk, the power construction risk level is determined to be high risk.

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