CN118117760A - Intelligent monitoring method and system for electrical load - Google Patents

Abstract

The invention relates to the technical field of electric load monitoring, and particularly discloses an intelligent monitoring method and system for electric loads, wherein the method comprises the following steps: abnormal management of a load area, matching of the current load bearing degree and matching of the maximum load bearing degree, connecting all electrical equipment of the area where the electrical load belongs through the Internet of things, judging operation monitoring abnormal indexes of all electrical equipment, and matching to obtain the current load bearing degree of the area where the electrical load belongs; and meanwhile, the historical operation influence indexes of the region where the electrical load belongs are obtained through integration, the maximum load bearing degree of the region where the electrical load belongs is obtained through matching, and is compared with the current load bearing degree of the region where the electrical load belongs, and finally, the region where the electrical load belongs is subjected to abnormal operation management, so that equipment load management and control personnel can monitor the load bearing degree capacity of the region where the electrical load belongs in real time, and the abnormal operation condition of the electrical equipment is timely adjusted, so that safe and stable operation of the electrical load is ensured.

Description

Intelligent monitoring method and system for electrical load

Technical Field

The invention relates to the technical field of electric load monitoring, in particular to an intelligent monitoring method and system for electric loads.

Background

Modern electrical load systems generally have complex structures and diversified working conditions, and meanwhile, the real-time monitoring requirements for the operation state of the electrical load are increasingly increased, so that a more intelligent monitoring method is needed to effectively manage the operation state of the electrical load to ensure the safe and stable operation of the electrical load system, the real-time remote management of the electrical load can be realized through the real-time monitoring of the electrical load, meanwhile, operators can know the operation condition of the electrical load at any time and adjust the operation condition in time, and through timely monitoring the bearing capacity of the electrical load, the abnormal condition of the electrical load can be timely found and corresponding measures can be taken, so that the safety of the electrical load is improved, the possibility of accidents is reduced, and finally, the energy utilization efficiency of the electrical load is maximized.

For example, the invention patent with the publication number of CN105989427B is a method for analyzing and early warning the state trend of equipment based on data mining, which comprises the following steps: determining a slow failure set of the equipment; establishing a mapping table of fault symptoms-electrical quantity variation trend; establishing a two-dimensional table of a device ID-device state change expression; the whole process monitoring is carried out on the state change of the equipment, the equipment fault evolution rule is mined by fully utilizing the experience knowledge of equipment monitoring and the massive historical data of the master station system through the provided equipment state trend analysis and early warning method based on data mining, the comprehensive perception of the running state of the equipment and the early warning of fault hidden danger are realized, the equipment monitoring is pushed to be changed from the conventional post passive monitoring to the pre active monitoring mode, the working pressure of monitoring personnel is relieved, and the running safety of a power grid is further ensured.

According to the technical scheme, when the electric load equipment is monitored, only real-time monitoring information of the electric equipment is analyzed, the regional environment affecting the stable operation of the electric equipment is not analyzed, meanwhile, the load operation abnormality degree of the electric equipment is not monitored and managed in a targeted mode, the difference between the equipment early warning result obtained through final analysis and the actual early warning result is large, the working pressure of monitoring staff is not reduced, and the running danger of the equipment in the region is increased.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides an intelligent monitoring method and system for an electric load, which can effectively solve the problems related to the background art.

In order to achieve the above purpose, the invention is realized by the following technical scheme: the first aspect of the invention provides an intelligent monitoring method for an electrical load, comprising the following steps: comparing the current load bearing degree of the area of the electric load with the maximum load bearing degree of the area of the electric load, so as to perform abnormal operation management on the area of the electric load; the current load bearing degree of the region to which the electrical load belongs is obtained by connecting all electrical equipment of the region to which the electrical load belongs through the Internet of things, monitoring the operation state of all the electrical equipment, acquiring operation monitoring data of all the electrical equipment, judging an operation monitoring abnormality index of all the electrical equipment, and matching the operation monitoring abnormality indexes; the maximum load bearing capacity of the region where the electrical load belongs is obtained by acquiring historical operation data corresponding to each electrical device of the region where the electrical load belongs, analyzing historical operation influence coefficients of each electrical device, and simultaneously carrying out environment detection on the region where the electrical load belongs, and integrating to obtain a historical operation influence index of the region where the electrical load belongs, so that the maximum load bearing capacity of the region where the electrical load belongs is obtained by matching.

As a further method, the operation anomaly management is performed on the area to which the electrical load belongs, and the specific management process is as follows:

Comparing the current load bearing degree of the region where the electric load belongs with the load maximum bearing degree of the region where the electric load belongs, and if the current load bearing degree of the region where the electric load belongs is higher than the load maximum bearing degree of the region where the electric load belongs, performing difference processing to obtain a load bearing difference value of the region where the electric load belongs; and matching the operation abnormality required management level corresponding to each load bearing difference interval defined in the load monitoring bin to obtain the operation abnormality required management level of the region to which the electric load belongs, so as to perform operation abnormality management on the region to which the electric load belongs.

As a further method, the matching obtains the current load bearing degree of the area to which the electrical load belongs, and the specific matching process is as follows:

Summing the operation monitoring abnormality indexes of all the electrical equipment to obtain an operation monitoring abnormality index of a region where the electrical load belongs; matching with the current load bearing degree of each operation monitoring abnormal index interval defined in the load monitoring bin, thereby obtaining the current load bearing degree of the region of the electric load.

As a further method, the matching obtains the maximum load bearing degree of the area to which the electrical load belongs, and the specific matching process is as follows:

According to the environment detection of the region of the electric load, environment influence data of the region of the electric load are obtained, so that environment influence coefficients of the region of the electric load are judged, and historical operation influence coefficients of all electric devices are integrated, so that historical operation influence indexes of the region of the electric load are obtained; and the load maximum bearing degree corresponding to each historical operation influence index interval defined in the load monitoring bin is matched, so that the load maximum bearing degree of the area where the electric load belongs is obtained.

As a further method, the operation monitoring abnormality index of each electrical device comprises the following specific analysis processes:

According to the operation monitoring data of each electrical device, load operation data and power quality data of each electrical device are extracted from the operation monitoring data, and load operation control degree values of each electrical device and power quality control degree values of each electrical device are respectively judged and obtained; and comprehensively processing the load operation control degree value of each electric device and the electric energy quality control degree value of each electric device, thereby obtaining the operation monitoring abnormality index of each electric device.

As a further method, the load operation control degree value of each electrical device is specifically analyzed as follows:

Extracting load operation data of each electrical device, wherein the load operation data comprises operation time length and execution load values at each execution time point; extracting rated execution time limit of each electrical device from the load monitoring bin, and performing ratio processing on the rated execution time limit and the operation time length of each electrical device, thereby obtaining the execution utilization rate of each electrical device; and extracting the execution reference load of each electric device from the load monitoring bin, and finally evaluating the load operation control degree value of each electric device.

As a further method, the electric energy quality control degree value of each electric device is specifically analyzed as follows:

According to the electric energy quality data of each electric device, wherein the electric energy quality data comprises a voltage value and a current value at each execution time point, and thus a voltage waveform curve and a current waveform curve of each electric device are constructed; according to the sag judgment voltage trigger value and the sag judgment current trigger value defined in the load monitoring bin, the voltage sag times and the current sag times of all the electrical equipment are counted; acquiring voltage amplitude values and current amplitude values of all the electric devices at all the execution time points, thus constructing voltage harmonic wave diagrams and current harmonic wave diagrams of all the electric devices, and acquiring voltage harmonic wave areas and current harmonic wave areas of all the electric devices; extracting voltage harmonic distortion degree corresponding to each voltage harmonic waveform area interval and current harmonic distortion degree corresponding to each current harmonic waveform area interval from a load monitoring bin, thereby obtaining voltage harmonic distortion degree and current harmonic distortion degree of each electrical device; and extracting the voltage sag allowable times and the current sag allowable times of each electrical device from the load monitoring bin, and integrating and evaluating the electric energy quality control degree value of each electrical device.

As a further method, the historical operation influence coefficient of each electrical device is specifically analyzed as follows:

Extracting historical operation data corresponding to each electrical device in a region to which an electrical load belongs, wherein the historical operation data comprises maintenance times, maintenance duration of each maintenance time, fault repair times and average fault repair interval time difference in a set historical operation period; the operation reliability of each electrical device in the set historical operation period is obtained by matching the maintenance times of each electrical device in the region where the electrical load belongs in the set historical operation period with the operation reliability corresponding to each maintenance time interval defined in the load monitoring bin; the fault influence degree of each electrical device in the set historical operation period is obtained by matching the fault repair times of each electrical device in the region where the electrical load belongs in the set historical operation period with the fault influence degree corresponding to each fault repair time interval defined in the load monitoring bin; and extracting maintenance reference time length and fault repair interval reference time difference of each electrical device from the load monitoring bin, and comprehensively analyzing historical operation influence coefficients of each electrical device.

As a further method, the environmental impact coefficient of the area to which the electrical load belongs, the specific analysis process is as follows:

According to environmental impact data of a region to which the electrical load belongs, wherein the environmental impact data comprises a maximum dust concentration in a set environmental impact period, an electromagnetic interference intensity value and an illumination intensity value at each impact time point; according to the maximum concentration of dust in a set environmental impact period of an area to which the electric load belongs, matching the air pollution degree corresponding to each dust concentration interval stored in the load monitoring bin to obtain the air pollution degree of the area to which the electric load belongs; and extracting an electromagnetic interference allowable intensity value and a maximum illumination intensity bearing value of the region to which the electric load belongs from the load monitoring bin, and comprehensively judging an environmental influence coefficient of the region to which the electric load belongs.

A second aspect of the present invention provides an intelligent monitoring system for an electrical load, comprising: the load region abnormality management module is used for comparing the current load bearing degree of the region to which the electric load belongs with the maximum load bearing degree of the region to which the electric load belongs, so as to perform operation abnormality management on the region to which the electric load belongs; the current load bearing degree matching module is used for the current load bearing degree of the region to which the electric load belongs, and is used for connecting all the electric devices of the region to which the electric load belongs through the Internet of things, monitoring the operation state of the electric devices, acquiring operation monitoring data of the electric devices, judging the operation monitoring abnormality index of the electric devices, and matching to obtain the current load bearing degree of the region to which the electric load belongs; the load maximum bearing degree matching module is used for obtaining the load maximum bearing degree of the region to which the electric load belongs, analyzing the historical operation influence coefficient of each electric device by acquiring the historical operation data corresponding to each electric device of the region to which the electric load belongs, and simultaneously carrying out environment detection on the region to which the electric load belongs, and integrating to obtain the historical operation influence index of the region to which the electric load belongs, so that the load maximum bearing degree of the region to which the electric load belongs is obtained by matching.

Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:

(1) The invention provides an intelligent monitoring method and system for an electric load, which are characterized in that the operation monitoring abnormality indexes of all electric devices are judged by connecting all electric devices in an area where the electric load belongs through the Internet of things, the operation condition of the electric devices can be monitored in real time, meanwhile, the historical operation influence indexes of the area where the electric load belongs are integrated, and finally, the operation abnormality management is carried out on the area where the electric load belongs, so that an equipment operator can timely adjust the operation abnormality condition of the electric load, and the safe and stable operation of an electric load system is ensured.

(2) According to the invention, the operation state of each electrical device is monitored by connecting the electrical devices in the area where the electrical load belongs to through the Internet of things, so that operation monitoring data of each electrical device is obtained, a load management and control person can obtain device operation information in real time, and judge the operation monitoring abnormality index of each electrical device, so that the current load bearing degree of the area where the electrical load belongs to is obtained by matching, and the load management and control person can timely take corresponding load abnormality countermeasures by monitoring the operation state of the electrical device, so that the safety of the electrical load is improved.

(3) According to the invention, through acquiring the historical operation data corresponding to each electric device in the area of the electric load, simultaneously carrying out environment detection on the area of the electric load, integrating to obtain the historical operation influence index of the area of the electric load, comprehensively analyzing the influence condition of the electric load, matching to obtain the maximum load bearing degree of the area of the electric load, comparing with the current load bearing degree of the area of the electric load, carrying out operation abnormality management on the area of the electric load, reducing the possibility of accident occurrence, and finally realizing the maximization of the energy utilization efficiency of the electric load.

Drawings

The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.

FIG. 1 is a flow chart of the method steps of the present invention.

Fig. 2 is a schematic diagram of system module connection according to the present invention.

Fig. 3 is a schematic diagram of a voltage waveform according to the present invention.

Fig. 4 is a schematic diagram of a current waveform according to the present invention.

Detailed Description

The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.

Referring to fig. 1, a first aspect of the present invention provides an intelligent monitoring method for an electrical load, including: comparing the current load bearing degree of the area of the electric load with the maximum load bearing degree of the area of the electric load, so as to perform abnormal operation management on the area of the electric load.

In this embodiment, the area to which the electric load belongs may be an area where electric equipment exists, such as a office building or a laboratory.

Specifically, the abnormal operation management is performed on the area to which the electrical load belongs, and the specific management process is as follows:

Comparing the current load bearing degree of the region where the electric load belongs with the load maximum bearing degree of the region where the electric load belongs, and if the current load bearing degree of the region where the electric load belongs is higher than the load maximum bearing degree of the region where the electric load belongs, performing difference processing to obtain a load bearing difference value of the region where the electric load belongs.

And matching the load bearing difference value of the region to which the electric load belongs with the operation abnormality required management level corresponding to each load bearing difference value region defined in the load monitoring bin to obtain the operation abnormality required management level of the region to which the electric load belongs, so as to perform operation abnormality management on the region to which the electric load belongs.

In one embodiment, the above-mentioned abnormal operation required management levels are divided into 10 levels, if the abnormal operation required management level of the area to which the electrical load belongs is 3 levels, the abnormal operation level of the area to which the electrical load belongs is light, and the abnormal operation management measures are as follows:

The real-time monitoring system is established for the region to which the electric load belongs, and the remote control technology is utilized to remotely operate and adjust the electric load so as to quickly respond and process smaller abnormal conditions; if the abnormal operation of the area of the electrical load is 8, the abnormal operation of the area of the electrical load is serious, and the abnormal operation management measures are as follows:

The operation state of the electric load is monitored in real time through a monitoring system of the area to which the electric load belongs, once abnormality occurs, fault diagnosis is carried out timely, the cause of the problem is determined, an emergency processing mechanism is established, a standby scheme and an emergency plan are formulated, and corresponding measures are ensured to be taken when the abnormality occurs; after the abnormal problems are solved, related personnel are trained, the capability of the related personnel for coping with the abnormal conditions of the electrical load is improved, and meanwhile, the system of the electrical equipment needs to be updated in time, so that the safety and reliability of the whole operation are improved.

The current load bearing degree of the region to which the electrical load belongs is obtained by connecting the electrical devices of the region to which the electrical load belongs through the Internet of things, monitoring the operation state of each electrical device, acquiring operation monitoring data of each electrical device, judging an operation monitoring abnormality index of each electrical device, and matching the operation monitoring abnormality indexes.

In this embodiment, the above-mentioned connecting, through the internet of things, each electrical device in the area to which the electrical load belongs, where the operation flow of the internet of things connection is as follows:

Ensuring that each electrical device has a corresponding Internet of things module which can be connected to the Internet of things, and checking the network connection performance and power supply of the electrical device; the Internet of things gateway equipment is deployed and used for connecting the electrical equipment and the Internet of things platform, and the gateway is configured to ensure that the gateway can communicate with each equipment; and starting an Internet of things module on each electrical device, starting to search available networks, connecting the electrical device to the Internet of things, inputting corresponding network information and credentials, and finally monitoring operation data of the electrical device through an Internet of things platform.

It should be noted that the above electrical devices include, but are not limited to, solar panels, transformers, distribution boxes, fans, compressors, load monitoring systems.

Specifically, the matching obtains the current load bearing degree of the area to which the electrical load belongs, and the specific matching process is as follows:

And summing the operation monitoring abnormality indexes of the electrical equipment to obtain the operation monitoring abnormality index of the area where the electrical load belongs.

And matching the operation monitoring abnormality index of the region to which the electric load belongs with the current load bearing degree of each operation monitoring abnormality index interval defined in the load monitoring bin, thereby obtaining the current load bearing degree of the region to which the electric load belongs.

In this embodiment, the operation monitoring abnormality index of the region to which the electrical load belongs is in an inverse relationship with the current load bearing degree of the region to which the electrical load belongs, that is, when the current load bearing degree of the region to which the electrical load belongs is reduced, the electrical load may be more susceptible to external interference or internal problems, resulting in an increase in the operation monitoring abnormality index.

Further, the operation monitoring abnormality index of each electrical device comprises the following specific analysis processes:

And according to the operation monitoring data of each electrical device, extracting load operation data and power quality data of each electrical device, and respectively judging and obtaining a load operation control degree value of each electrical device and a power quality control degree value of each electrical device.

In one embodiment, the machine learning algorithm may be utilized to train and predict the data of the electrical devices to identify anomalies, thereby obtaining an operational monitoring anomaly index for each electrical device.

In this embodiment, the load operation control degree value of each electrical device and the power quality control degree value of each electrical device are comprehensively processed to obtain data about the operation monitoring abnormality degree of each electrical device, and the operation monitoring abnormality degree of each electrical device is obtained by adopting a more accurate calculation method, wherein the specific calculation method is as follows:

，

In the method, in the process of the invention, Monitoring an abnormality index for the operation of the b-th electrical device, in this embodiment, poor load operation control may cause overload of a part of devices and low load of other devices, resulting in unbalanced load of the devices, increasing risk of damage to the devices, and meanwhile, improper load control may cause overload operation of the system, resulting in overheating and damage of the devices, even causing accidents, and increasing risk degree of operation of the load devices; the poor electric energy quality can cause unstable operation of equipment, influence the normal operation of the equipment, increase the possibility of equipment failure, influence the efficiency of a power system due to the poor electric energy quality, increase energy loss and reduce the overall operation efficiency of the system; in summary, the adverse effects of the load operation control level value and the power quality control level value may aggravate the operation monitoring abnormality level of each electrical device, thereby increasing the risk of the system and reducing the reliability of the system, so that it is important to effectively control and manage the load operation and the power quality, so as to ensure the normal operation of the electrical device and the stability of the system.

Load operation control degree value representing the b-th electrical device,/>Indicating the power quality control degree value of the b-th electrical device,/>Weight index of preset load operation control degree value,/>The weight index is the weight index which is the preset power quality control degree value.

Specifically, the load operation control degree value of each electrical device is specifically analyzed as follows:

Load operation data of each electrical device is extracted, wherein the load operation data comprises an operation time length and an execution load value at each execution time point.

It should be explained that, in the above-mentioned method for obtaining the operation time length, a remote monitoring system is generally used to obtain the operation state of the electrical device, and the system may provide the operation time length information of the device, so as to obtain the operation time length of each electrical device; the execution load value is obtained by a sensor installed in the power system to obtain the execution load value of the electrical equipment at each execution time point.

The above execution time points are obtained by disposing the set device execution period as each execution time point.

And extracting rated execution time limit of each electrical device from the load monitoring bin, and performing ratio processing on the rated execution time limit and the operation time length of each electrical device, thereby obtaining the execution utilization rate of each electrical device.

And extracting the execution reference load of each electrical device from the load monitoring bin, and finally evaluating the load operation control degree value of each electrical device.

The load operation control degree value of each electrical device is obtained by comprehensively analyzing the execution load value and the execution utilization rate in the embodiment, and the embodiment adopts a more accurate calculation method, wherein the specific calculation method is as follows:

，

In the method, in the process of the invention, A load operation control degree value indicating the b-th electrical device, in which, in the present embodiment, if the execution load value is too high or the execution utilization rate is too high, the electrical device may be overloaded, which may cause overheating, damage, or even fire occurrence of safety problems of the electrical device; the high load can cause the equipment to frequently run in a high load state, so that the aging and damage of the electrical equipment are accelerated, the service life of the equipment is shortened, and the maintenance and replacement cost is increased; therefore, maintaining an appropriate execution load value and execution utilization rate is critical to ensuring normal operation of electrical equipment and ensuring safe and stable operation of the power system.

Representing a control evaluation correction value to which a preset execution load value belongs,/>Control influence factor indicating a preset execution utilization ratio corresponding to a unit value,/>Representing an execution load value of the b-th electrical device at the A-th execution time point, wherein the execution load value represents the power load magnitude born by the electrical device at each execution time point, and the execution load value is usually represented by power and is used for describing the current power demand condition of the electrical device.

Representing the execution reference load of the b-th electrical device, wherein the execution reference load represents the desired load level required by the electrical device during the device execution period, typically a reference value determined during system operation, for guiding device operation and scheduling.

Representing a preset execution load allowable deviation value, representing a difference allowable maximum value between an actual execution load value and an execution reference load.

The execution utilization rate of the b-th electrical equipment is the ratio between the operation time length of the electrical equipment and the rated execution time limit, the ratio reflects the relation between the actual utilization condition of the electrical equipment in the equipment execution time period and the availability time of the electrical equipment, and the ratio is one of indexes for evaluating the utilization efficiency and the production benefit of the electrical equipment.

It should be noted that, the above operation time period refers to a time period from an initial operation time point of the electrical device to an end time point of the device execution period.

A is the number of each execution time point,E is the number of execution time points, b represents the number of each electrical device,/>Y represents the number of electrical devices, and e is a natural constant.

Further, the specific analysis process of the electric energy quality control degree value of each electric device is as follows:

and constructing a voltage waveform curve and a current waveform curve of each electrical device according to the power quality data of each electrical device, wherein the power quality data comprises the voltage value and the current value at each execution time point.

The voltage value and the current value at each execution time point are required to be described, wherein the voltage value is generally obtained by measuring a voltmeter, and the voltmeter is directly connected to electrical equipment in an electrical system to monitor and display the voltage value in real time; the current value is usually obtained through a current transformer, which can convert a current signal in a circuit into a current value which can be measured and recorded, and transmit the data to a monitoring system.

It should be further noted that, the voltage waveform curve and the current waveform curve of each electrical device are shown in fig. 3, where the abscissa is the execution time point, the unit is seconds, the ordinate is the voltage value, and the unit is volts; the current waveform curve is shown in fig. 4, with the abscissa representing the execution time point, the unit being seconds, the ordinate representing the current value, and the unit being amperes.

And judging a voltage trigger value and a current trigger value according to the voltage waveform curve and the current waveform curve of each electrical device and according to the sag defined in the load monitoring bin, thereby counting the voltage sag times and the current sag times of each electrical device.

It should be noted that the sag judgment voltage trigger value refers to a value marked by a voltage value lower than a predetermined voltage sag threshold, and the sag judgment current trigger value refers to a value marked by a current value lower than a predetermined current sag threshold.

It should be further explained that the above-mentioned method for obtaining the number of voltage sag times is to count the number of corresponding execution time points when the voltage value at each execution time point is lower than the sag judgment voltage trigger value, and record the number as the number of voltage sag times; the current dip times are obtained by counting the number of corresponding execution time points when the current value at each execution time point is lower than the dip judgment current trigger value, and recording the number as the current dip times.

Acquiring voltage amplitude values and current amplitude values of all the electrical equipment at all the execution time points, and thus constructing a voltage harmonic wave diagram and a current harmonic wave diagram of all the electrical equipment, wherein the voltage amplitude value acquisition mode can provide accurate voltage amplitude value reading through a voltage sensor, and monitoring the voltage change condition of the electrical equipment in real time; the current amplitude can provide accurate current amplitude reading through the current sensor, and the current change condition of the electrical equipment is monitored in real time.

It should be explained that, the abscissa of the voltage harmonic waveform chart is the execution time point, the unit is seconds, the ordinate is the voltage amplitude, and the unit is volts; the abscissa of the current harmonic waveform plot is the execution time point in seconds, the ordinate is the current amplitude, and the unit is amperes.

From which the voltage harmonic waveform area and the current harmonic waveform area of each electrical device are obtained.

It should be noted that, the above-mentioned voltage harmonic waveform area is obtained by taking a given voltage harmonic waveform chart as a function, wherein the execution time point is an independent variable, performing an integration operation on the function to give an area under the waveform, and using a numerical integration method to approximate the area under the waveform, thereby obtaining the voltage harmonic waveform area; the method for obtaining the area of the current harmonic waveform is to consider a given current harmonic waveform diagram as a function, wherein the execution time point is an independent variable, integrate the function, calculate the integration on a time axis to obtain the area under the waveform, and approximate the area under the current waveform by using a numerical integration method to obtain the area of the current harmonic waveform.

And extracting the voltage harmonic distortion degree corresponding to each voltage harmonic waveform area section and the current harmonic distortion degree corresponding to each current harmonic waveform area section from the load monitoring bin, thereby obtaining the voltage harmonic distortion degree and the current harmonic distortion degree of each electrical device.

The allowable number of voltage sag and allowable number of current sag of each electrical device are extracted from the load monitoring bin, and the power quality control degree value of each electrical device is integrated and evaluated.

In this embodiment, the voltage harmonic distortion degree, the current harmonic distortion degree, the voltage sag times and the current sag times are comprehensively determined to obtain the data about the power quality control degree of each electrical device, and the data are obtained by adopting a more accurate calculation method, wherein the specific calculation method is as follows:

，

In the method, in the process of the invention, The electric energy quality control degree value representing the b-th electric device, in this embodiment, the high degree of harmonic distortion of the voltage and the current may cause the performance of the electric device to be reduced, and particularly for precision devices and electronic devices, the normal operation and the service life thereof may be affected; the voltage sag and the current sag can cause the energy efficiency of electrical equipment to be reduced, influence the operation efficiency of a system and increase the energy consumption; meanwhile, frequent voltage sag and current sag can accelerate equipment aging, shorten equipment life and increase maintenance cost; monitoring and controlling these indicators is therefore critical to ensuring power quality.

Influence factor representing the corresponding unit value of the predefined voltage harmonic distortion,/>Influence factor representing the corresponding unit value of the predefined current harmonic distortionThe voltage harmonic distortion degree of the b-th electrical device is an index for measuring the harmonic content in a voltage waveform, the voltage waveform contains harmonic components with different frequencies, and the harmonic components can cause distortion of the voltage waveform and affect the power quality.

As for the current harmonic distortion of the b-th electrical device, the current harmonic distortion in this embodiment is an index for measuring the harmonic content in the current waveform, and similarly to the voltage harmonic distortion, the current harmonic distortion is also represented by the total harmonic distortion.

Control assessment factor corresponding to the predefined number of voltage dips,/>Control assessment factor representing a predefined number of current dips,/>, correspondingThe number of voltage dips for the b-th electrical device, wherein the number of voltage dips represents a phenomenon that voltage drops to a lower level in a short time in the electrical device, and the duration is relatively short, is used to describe the number of voltage dip events occurring in the device execution period.

The voltage sag allowable number in this embodiment refers to the highest allowable voltage sag number for the b-th electrical device.

The number of current dips for the b-th electrical device, wherein the number of current dips is the number of continuous decreases in current in the statistical electrical device.

The current dip allowable number in this embodiment indicates the highest number of allowed current dips for the current dip allowable number of the b-th electrical device.

B represents the number of each electrical device,Y represents the number of electrical devices.

In a specific embodiment, the invention monitors the operation state of each electrical device by connecting each electrical device in the area where the electrical load belongs to through the internet of things to acquire operation monitoring data of each electrical device, so that a load management and control personnel can acquire device operation information in real time and judge operation monitoring abnormality indexes of each electrical device, thereby matching to obtain the current load bearing degree of the area where the electrical load belongs to, and by monitoring the operation state of the electrical device in real time, the load management and control personnel can timely take corresponding load abnormality countermeasures to improve the safety of the electrical load.

The maximum load bearing capacity of the region where the electrical load belongs is obtained by acquiring historical operation data corresponding to each electrical device of the region where the electrical load belongs, analyzing historical operation influence coefficients of each electrical device, and simultaneously carrying out environment detection on the region where the electrical load belongs, and integrating to obtain a historical operation influence index of the region where the electrical load belongs, so that the maximum load bearing capacity of the region where the electrical load belongs is obtained by matching.

In this embodiment, the maximum load bearing capacity refers to the maximum severe environment and the maximum load operating state that the region to which the electrical load belongs can bear under the historical operating condition.

Specifically, the matching obtains the maximum load bearing degree of the area to which the electrical load belongs, and the specific matching process is as follows:

and according to the environment detection of the region to which the electrical load belongs, obtaining the environment influence data of the region to which the electrical load belongs.

In this embodiment, the specific acquisition process of the environmental impact data of the area to which the electrical load belongs is as follows:

firstly, determining the area range of the analyzed electrical load, and researching the environmental influence related to the area through an environmental organization mechanism or searching and acquiring information by using an online data platform; once the environmental impact data is obtained, the source, accuracy and reliability of the data are ensured to be inspected and analyzed, and finally the environmental impact data of the area where the electrical load belongs is obtained.

In this way, the environmental impact coefficient of the area where the electrical load belongs is determined, and the historical operation impact coefficients of the electrical devices are integrated, so as to obtain the historical operation impact index of the area where the electrical load belongs.

In this embodiment, the environmental impact coefficient of the area to which the electrical load belongs and the historical operation impact coefficient of each electrical device are integrated to obtain data about the historical operation impact degree of the area to which the electrical load belongs, and the embodiment adopts a more accurate calculation method to obtain the historical operation impact index, wherein the specific calculation method is as follows:

，

In the method, in the process of the invention, As the historical operation influence index of the area to which the electrical load belongs, in this embodiment, if the historical operation influence coefficient of each electrical device is high, it may mean that there is a high risk of aging and failure of the devices, so that the devices are frequently damaged or stopped, and the reliability and stability of the power supply are affected; meanwhile, if the environmental impact coefficient of the area where the electric load belongs is higher, the electric load may mean that more pollutant emission and energy waste are generated in the process of power production and use, and negative influence is caused on the environment; therefore, in order to reduce the degree of influence and negative influence of the historical operation of the area to which the electrical load belongs, a series of measures such as periodic maintenance of equipment, improvement of the operating efficiency of the equipment, optimization of energy utilization, enhancement of environmental management and monitoring can be taken, and by effective management and monitoring, the negative influence can be reduced, and the operating efficiency and environmental friendliness of the area to which the electrical load belongs can be improved.

For the historic operation influence coefficient of the b-th electrical device,/>Is the environmental impact coefficient of the area to which the electrical load belongs,/>Representing the weight value of the set historical operation influence coefficient,/>The set weight of the environmental impact coefficient is indicated. /(I)

And matching the historical operation influence indexes of the region to which the electric load belongs with the load maximum bearing degree corresponding to each historical operation influence index interval defined in the load monitoring bin to obtain the load maximum bearing degree of the region to which the electric load belongs.

In this embodiment, the historical operation impact index of the region to which the electrical load belongs is in a proportional relationship with the maximum load bearing capacity of the region to which the electrical load belongs, that is, if the historical operation impact index is higher, the capacity of the electrical equipment to bear the load fault factor may be improved, so that the maximum load bearing capacity of the region to which the electrical load belongs is increased.

Further, the historical operation influence coefficient of each electrical device comprises the following specific analysis processes:

and extracting historical operation data corresponding to each electrical device in the area where the electrical load belongs, wherein the historical operation data comprises maintenance times, maintenance duration of each maintenance, fault repair times and average fault repair interval time difference in a set historical operation period.

It should be explained that, the above-mentioned maintenance frequency obtaining mode is that maintenance personnel can record information such as maintenance date, maintenance content, maintenance time and the like after each maintenance, so that the maintenance frequency in the historical operation period can be counted; the maintenance time length of each maintenance is obtained by comparing the time difference before and after the maintenance in the maintenance period; the method for obtaining the fault repair times is to obtain the times of equipment faults in a historical operation period through an established fault recording system; the average fault repair interval time difference is obtained by tracking fault repair time information through an inventory management system, obtaining the interval time of next fault repair corresponding to each fault repair, and obtaining the average fault repair interval time difference through average value processing.

The operation reliability of each electrical device in the set historical operation period is obtained by matching the maintenance times of each electrical device in the region where the electrical load belongs in the set historical operation period with the operation reliability corresponding to each maintenance time interval defined in the load monitoring bin.

And matching the fault influence degree corresponding to each fault repair time interval defined in the load monitoring bin according to the fault repair times of each electrical equipment in the area where the electrical load belongs in the set historical operation period, so as to obtain the fault influence degree of each electrical equipment in the set historical operation period.

The maintenance reference time length and the fault repair interval reference time difference of each electrical device are extracted from the load monitoring bin, the historical operation influence coefficients of each electrical device are comprehensively analyzed, in a specific embodiment, the electrical devices can be divided into different groups according to the similarity of the historical operation influence coefficients through cluster analysis, understanding of the characteristics of the devices and the change rule of the influence coefficients is facilitated, and therefore the historical operation influence coefficients of each electrical device are obtained.

The above-mentioned historical operation influence coefficient of each electrical device is obtained by comprehensively determining the maintenance duration, the average fault repair interval time difference, the operation reliability and the fault influence degree in this embodiment, and the historical operation influence coefficient of each electrical device is obtained by adopting a more accurate calculation method, which is specifically as follows:

，

In the method, in the process of the invention, As the historical operation influence coefficient of the b-th electrical device, in the embodiment, high maintenance duration and frequent fault repair may accelerate wear of the electrical device, shortening the life of the electrical device; the high fault influence can cause unstable operation of electrical equipment, increase downtime and influence the stability and productivity of a production line; unstable operation reliability may reduce production efficiency; in summary, the maintenance duration, the average fault repair interval time difference, the operation reliability and the degree of influence of the fault on the historical operation of the electrical equipment may have a series of adverse effects such as increased cost, shortened service life of the equipment, unstable operation, increased safety risk, reduced production efficiency and difficult maintenance if the degree of influence is high, so it is very important to effectively manage and optimize these factors.

Indicating the length of time that the b-th electrical device was serviced at the c-th service, where the length of time typically refers to the length of time that it takes to perform the service, when the service is performed, the electrical device may need to run a particular system or component to perform inspection, cleaning, calibration, or other service operations.

Representing the average time difference between fail-over intervals for the b-th electrical device over a set historical operating period, wherein the average time difference between fail-over intervals is typically indicative of the extent of the intervals in which fail-over time is occurring, can help assess the cost of maintenance work, the use of the accessories, and the nature and frequency of device failures.

The operational reliability of the b-th electrical device in the set historical operation period is indicated, wherein the operational reliability refers to the capability and stability of the electrical device in normal operation in the historical operation period, and is generally in inverse relation with the maintenance times of the electrical device, namely, the more the maintenance times are, the lower the operational reliability of the electrical device is indicated.

The failure influence degree of the b-th electrical equipment in a set historical operation period is represented, wherein the failure influence degree represents the influence degree of failure frequency of the electrical equipment in the historical operation period, and the index is generally used for measuring the reliability and stability of the equipment and reflecting the influence degree of failure of the equipment in the operation process.

The maintenance reference time period is the b-th electric device, wherein the electricity consumption reference amount refers to a standard value specified when analyzing the maintenance time period of the electric device.

The time difference is referred to for the fault recovery interval of the b-th electrical device, wherein the fault recovery interval reference time difference represents an optimal standard value specified when analyzing the fault recovery interval time of the electrical device.

Evaluating and correcting coefficient corresponding to preset maintenance duration,/>Evaluating correction factors corresponding to preset accessory consumption number,/>An operation influence factor corresponding to a unit value for a preset operation reliability,/>An operation influence factor corresponding to a unit value for a preset fault influence degree, b represents the number of each electric device, and/(m)Y represents the number of electrical devices, c represents the number of maintenance operations, and "/>G represents the number of maintenance operations and e is a natural constant.

Specifically, the environmental impact coefficient of the area to which the electrical load belongs is determined by the following specific analysis process:

The set environmental impact period is deployed as each impact time point, and according to the environmental impact data of the area to which the electrical load belongs, the environmental impact data comprises the maximum concentration of dust in the set environmental impact period, the electromagnetic interference intensity value at each impact time point and the illumination intensity value.

The method for obtaining the maximum dust concentration includes the steps of sampling and monitoring dust in air by using a dust meter, obtaining dust concentration data of an area where an electric load belongs at a plurality of time points, and obtaining the maximum dust concentration by screening; the electromagnetic interference intensity value can be obtained by measuring the intensity of the whole electromagnetic radiation by an electromagnetic radiation sensor, wherein the electromagnetic radiation comprises the radiation from the electric equipment and the radiation of other electromagnetic sources; the illumination intensity value is obtained by monitoring the illumination intensity in real time through an illumination sensor and recording the value at each influence time point.

And matching the air pollution degree corresponding to each dust concentration interval stored in the load monitoring bin according to the maximum dust concentration of the region of the electric load in a set environment influence period to obtain the air pollution degree of the region of the electric load.

The electromagnetic interference allowable intensity value and the illumination intensity maximum bearing value of the area of the electric load are extracted from the load monitoring bin, and the environmental impact coefficient of the area of the electric load is comprehensively determined.

The environmental impact coefficient of the area to which the electrical load belongs is obtained by integrating and evaluating the air pollution degree, the electromagnetic interference intensity value and the illumination intensity value in the embodiment, and the data about the environmental impact degree of the area to which the electrical load belongs is obtained by adopting a more accurate calculation method, wherein the specific calculation method is as follows:

，

wherein, As an environmental impact coefficient of a region to which an electrical load belongs, in this embodiment, chemical substances and particulate matters in a highly polluted environment may cause equipment insulation materials to age, reduce insulation performance, and increase risk of equipment failure; the high-strength electromagnetic interference can interfere with electronic equipment, communication equipment and the like, and the normal operation and performance of the electronic equipment, the communication equipment and the like are affected; some electrical devices may contain photosensitive elements, such as photosensors, etc., and variations in illumination intensity may affect the performance of these elements; therefore, monitoring and controlling air pollution levels, electromagnetic interference intensity values and illumination intensity values are critical to maintaining the environmental quality of the area to which the electrical load belongs, and necessary measures need to be taken to reduce these negative effects.

In the area of electric loadElectromagnetic interference intensity values at the time points of influence, wherein the electromagnetic interference intensity values represent the areas where the electric loads belong, reflect the electromagnetic radiation degree generated by electronic equipment, communication equipment and the like in the areas, and possibly generate interference due to the influence of external factors such as high-voltage transmission lines, radio transmission towers and the like.

The electromagnetic interference allowable intensity value is an electromagnetic interference allowable intensity value of a region to which the electric load belongs, wherein the electromagnetic interference allowable intensity value refers to a maximum allowable value set when analyzing the electromagnetic interference intensity value.

Correction factor corresponding to predefined electromagnetic interference intensity value,/>Indicating that the region to which the electrical load belongs is at the/>The illumination intensity value at each influence time point, wherein the illumination intensity value refers to the illumination condition in the area where the electrical load belongs, namely the brightness degree of light rays, and the measurement and monitoring of the illumination intensity value can help to evaluate the performance of the illumination system and ensure the illumination quality of indoor and outdoor environments.

The maximum light intensity tolerance value of the region to which the electrical load belongs is represented, wherein the maximum light intensity tolerance value refers to the highest allowable value specified when evaluating the light intensity value.

Representing a correction factor corresponding to a predefined illumination intensity value,/>The air pollution level of the area to which the electrical load belongs is expressed, wherein the air pollution level refers to the concentration level of pollution in the air in the area. /(I)

Environmental impact factor representing predefined air pollution levels corresponding to unit values,/>Number indicating each influence time point,/>The number of the influence time points is represented.

In a specific embodiment, the method and the device for achieving the energy utilization efficiency maximization of the electric load can comprehensively analyze the influence condition of the electric load by acquiring the historical operation data corresponding to each electric device in the area where the electric load belongs, simultaneously carrying out environment detection on the area where the electric load belongs, integrating the historical operation influence indexes of the area where the electric load belongs, obtaining the maximum load bearing degree of the area where the electric load belongs through matching, comparing the maximum load bearing degree with the current load bearing degree of the area where the electric load belongs, carrying out operation abnormality management on the area where the electric load belongs, reducing the possibility of accidents, and finally achieving the energy utilization efficiency maximization of the electric load.

Referring to fig. 2, a second aspect of the present invention provides an intelligent monitoring system for an electrical load, comprising: the load area abnormality management module, the current load bearing degree matching module and the load maximum bearing degree matching module.

The second aspect of the present invention provides an intelligent monitoring system for an electrical load, further comprising a load monitoring bin, wherein the load monitoring bin is configured to store an operation abnormality required management level corresponding to each load bearing difference interval, a current load bearing degree to which each operation monitoring abnormality index interval belongs, a load maximum bearing degree corresponding to each historical operation influence index interval, a voltage harmonic distortion degree corresponding to each voltage harmonic waveform area interval, a current harmonic distortion degree corresponding to each current harmonic waveform area interval, an operation reliability corresponding to each maintenance frequency interval, a fault influence loudness corresponding to each fault repair frequency interval, an air pollution degree corresponding to each dust concentration interval, and store a rated execution time limit, an execution reference load, a voltage sag permission frequency, a current sag permission frequency, a maintenance reference time length, a fault repair interval reference time difference of each electrical device, and store a sag judgment voltage trigger value, a sag judgment current trigger value, an electromagnetic interference permission intensity value of an area to which the electrical load belongs, and an illumination intensity maximum bearing value.

It should be explained that the various information stored in the load monitoring bin is information according to the model, type, appearance, historical operation data and the like of the electrical equipment, and is processed through data analysis, so that various information suitable for the electrical load is obtained.

The load area abnormality management module, the current load bearing degree matching module and the load maximum bearing degree matching module are connected with the load monitoring bin, and the current load bearing degree matching module and the load maximum bearing degree matching module are connected with the load area abnormality management module.

The load region abnormality management module is used for comparing the current load bearing degree of the region where the electric load belongs with the load maximum bearing degree of the region where the electric load belongs, so as to perform operation abnormality management on the region where the electric load belongs.

The current load bearing degree matching module is used for the current load bearing degree of the region to which the electric load belongs, and is used for connecting all the electric devices of the region to which the electric load belongs through the Internet of things, monitoring the operation state of the electric devices, acquiring operation monitoring data of the electric devices, judging the operation monitoring abnormality index of the electric devices, and matching to obtain the current load bearing degree of the region to which the electric load belongs.

The load maximum bearing degree matching module is used for the load maximum bearing degree of the region where the electric load belongs, and is used for analyzing the historical operation influence coefficients of all the electric devices by acquiring the historical operation data corresponding to all the electric devices of the region where the electric load belongs, and simultaneously carrying out environment detection on the region where the electric load belongs, and integrating to obtain the historical operation influence index of the region where the electric load belongs, so that the load maximum bearing degree of the region where the electric load belongs is obtained through matching.

In a specific embodiment, the invention provides an intelligent monitoring method and system for an electric load, which are used for judging the operation monitoring abnormality indexes of all electric devices by connecting all electric devices in an area where the electric load belongs through the Internet of things, and can be used for monitoring the operation condition of the electric devices in real time, simultaneously integrating and obtaining the historical operation influence indexes of the area where the electric load belongs, and finally carrying out operation abnormality management on the area where the electric load belongs, so that equipment operators can timely adjust the operation abnormality conditions of the electric load to ensure safe and stable operation of an electric load system.

The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.

Claims (8)

1. An intelligent monitoring method for an electrical load, comprising:

comparing the current load bearing degree of the area of the electric load with the maximum load bearing degree of the area of the electric load, so as to perform abnormal operation management on the area of the electric load;

The current load bearing degree of the region to which the electrical load belongs is obtained by connecting all electrical equipment of the region to which the electrical load belongs through the Internet of things, monitoring the operation state of all the electrical equipment, acquiring operation monitoring data of all the electrical equipment, judging an operation monitoring abnormality index of all the electrical equipment, and matching the operation monitoring abnormality indexes;

The method comprises the steps of analyzing historical operation influence coefficients of all electrical equipment by acquiring historical operation data corresponding to all electrical equipment in an area where the electrical load belongs, simultaneously carrying out environment detection on the area where the electrical load belongs, integrating to obtain a historical operation influence index of the area where the electrical load belongs, and matching to obtain the maximum load bearing degree of the area where the electrical load belongs;

The matching obtains the current load bearing degree of the area to which the electrical load belongs, and the specific matching process is as follows:

Summing the operation monitoring abnormality indexes of all the electrical equipment to obtain an operation monitoring abnormality index of a region where the electrical load belongs;

matching the operation monitoring abnormality index of the region to which the electric load belongs with the current load bearing degree of each operation monitoring abnormality index interval defined in the load monitoring bin, thereby obtaining the current load bearing degree of the region to which the electric load belongs;

The operation monitoring abnormality index of each electrical device comprises the following specific analysis processes:

according to the operation monitoring data of each electrical device, load operation data and power quality data of each electrical device are extracted from the operation monitoring data, and load operation control degree values of each electrical device and power quality control degree values of each electrical device are respectively judged and obtained;

Comprehensively processing the load operation control degree value of each electrical device and the electric energy quality control degree value of each electrical device, thereby obtaining an operation monitoring abnormality index of each electrical device, wherein the analysis formula is as follows:

，

In the method, in the process of the invention, Monitoring abnormality index for operation of b-th electrical device,/>Load operation control degree value representing the b-th electrical device,/>Indicating the power quality control degree value of the b-th electrical device,/>Weight index of preset load operation control degree value,/>The weight index is the weight index which is the preset power quality control degree value.

2. An intelligent monitoring method for electrical loads according to claim 1, wherein: the operation abnormality management is carried out on the region to which the electrical load belongs, and the specific management process is as follows:

comparing the current load bearing degree of the region where the electric load belongs with the load maximum bearing degree of the region where the electric load belongs, and if the current load bearing degree of the region where the electric load belongs is higher than the load maximum bearing degree of the region where the electric load belongs, performing difference processing to obtain a load bearing difference value of the region where the electric load belongs;

And matching the load bearing difference value of the region to which the electric load belongs with the operation abnormality required management level corresponding to each load bearing difference value region defined in the load monitoring bin to obtain the operation abnormality required management level of the region to which the electric load belongs, so as to perform operation abnormality management on the region to which the electric load belongs.

3. An intelligent monitoring method for electrical loads according to claim 1, wherein: the matching obtains the maximum load bearing degree of the area to which the electrical load belongs, and the specific matching process is as follows:

According to the environment detection of the region of the electric load, environment influence data of the region of the electric load are obtained, so that environment influence coefficients of the region of the electric load are judged, and historical operation influence coefficients of all electric devices are integrated, so that historical operation influence indexes of the region of the electric load are obtained;

And matching the historical operation influence indexes of the region to which the electric load belongs with the load maximum bearing degree corresponding to each historical operation influence index interval defined in the load monitoring bin to obtain the load maximum bearing degree of the region to which the electric load belongs.

4. An intelligent monitoring method for electrical loads according to claim 1, wherein: the load operation control degree value of each electrical device comprises the following specific analysis processes:

Extracting load operation data of each electrical device, wherein the load operation data comprises operation time length and execution load values at each execution time point;

Extracting rated execution time limit of each electrical device from the load monitoring bin, and performing ratio processing on the rated execution time limit and the operation time length of each electrical device, thereby obtaining the execution utilization rate of each electrical device;

and extracting the execution reference load of each electric device from the load monitoring bin, and finally evaluating the load operation control degree value of each electric device.

5. The intelligent monitoring method for electrical loads according to claim 4, wherein: the electric energy quality control degree value of each electric device comprises the following specific analysis processes:

according to the electric energy quality data of each electric device, wherein the electric energy quality data comprises a voltage value and a current value at each execution time point, and thus a voltage waveform curve and a current waveform curve of each electric device are constructed;

Judging a voltage trigger value and a current trigger value according to the voltage waveform curve and the current waveform curve of each electrical device and according to the sag defined in the load monitoring bin, thereby counting the voltage sag times and the current sag times of each electrical device;

acquiring voltage amplitude values and current amplitude values of all the electric devices at all the execution time points, thus constructing voltage harmonic wave diagrams and current harmonic wave diagrams of all the electric devices, and acquiring voltage harmonic wave areas and current harmonic wave areas of all the electric devices;

Extracting voltage harmonic distortion degree corresponding to each voltage harmonic waveform area interval and current harmonic distortion degree corresponding to each current harmonic waveform area interval from a load monitoring bin, thereby obtaining voltage harmonic distortion degree and current harmonic distortion degree of each electrical device;

And extracting the voltage sag allowable times and the current sag allowable times of each electrical device from the load monitoring bin, and integrating and evaluating the electric energy quality control degree value of each electrical device.

6. An intelligent monitoring method for electrical loads according to claim 3, wherein: the historical operation influence coefficients of the electrical equipment are specifically analyzed as follows:

Extracting historical operation data corresponding to each electrical device in a region to which an electrical load belongs, wherein the historical operation data comprises maintenance times, maintenance duration of each maintenance time, fault repair times and average fault repair interval time difference in a set historical operation period;

the operation reliability of each electrical device in the set historical operation period is obtained by matching the maintenance times of each electrical device in the region where the electrical load belongs in the set historical operation period with the operation reliability corresponding to each maintenance time interval defined in the load monitoring bin;

The fault influence degree of each electrical device in the set historical operation period is obtained by matching the fault repair times of each electrical device in the region where the electrical load belongs in the set historical operation period with the fault influence degree corresponding to each fault repair time interval defined in the load monitoring bin;

And extracting maintenance reference time length and fault repair interval reference time difference of each electrical device from the load monitoring bin, and comprehensively analyzing historical operation influence coefficients of each electrical device.

7. An intelligent monitoring method for electrical loads according to claim 3, wherein: the environment influence coefficient of the area where the electrical load belongs comprises the following specific analysis processes:

according to environmental impact data of a region to which the electrical load belongs, wherein the environmental impact data comprises a maximum dust concentration in a set environmental impact period, an electromagnetic interference intensity value and an illumination intensity value at each impact time point;

according to the maximum concentration of dust in a set environmental impact period of an area to which the electric load belongs, matching the air pollution degree corresponding to each dust concentration interval stored in the load monitoring bin to obtain the air pollution degree of the area to which the electric load belongs;

And extracting an electromagnetic interference allowable intensity value and a maximum illumination intensity bearing value of the region to which the electric load belongs from the load monitoring bin, and comprehensively judging an environmental influence coefficient of the region to which the electric load belongs.

8. An intelligent monitoring system for electrical loads, which adopts the intelligent monitoring method for electrical loads according to any one of claims 1 to 7, and is characterized in that: comprising the following steps:

The load region abnormality management module is used for comparing the current load bearing degree of the region to which the electric load belongs with the maximum load bearing degree of the region to which the electric load belongs, so as to perform operation abnormality management on the region to which the electric load belongs;

The current load bearing degree of the region to which the electrical load belongs is obtained by connecting all electrical equipment of the region to which the electrical load belongs through the Internet of things, monitoring the operation state of all the electrical equipment, acquiring operation monitoring data of all the electrical equipment, judging an operation monitoring abnormality index of all the electrical equipment, and matching the operation monitoring abnormality indexes;

the maximum load bearing capacity of the region where the electrical load belongs is obtained by acquiring historical operation data corresponding to each electrical device of the region where the electrical load belongs, analyzing historical operation influence coefficients of each electrical device, and simultaneously carrying out environment detection on the region where the electrical load belongs, and integrating to obtain a historical operation influence index of the region where the electrical load belongs, so that the maximum load bearing capacity of the region where the electrical load belongs is obtained by matching.