Electric energy quality management monitoring system and method based on Internet of Things

Abstract

The invention relates to the technical field of power grid monitoring, in particular to a power quality management monitoring system and method based on the Internet of Things, and the monitoring method comprises the steps: S1, obtaining the evaluation index data of a monitoring region through a sensing layer; s2, evaluating the electric energy quality of the monitoring area; step S3, when the evaluation result is abnormal, acquiring the coverage area of the accessed adjusting equipment in the monitoring area and fault information occurring in the monitoring area, and judging whether new adjusting equipment needs to be accessed or not; and S4, if a new adjusting device needs to be accessed, outputting a region recommended to be accessed to the adjusting device. According to the method, whether new adjusting equipment needs to be connected or not is accurately judged through analysis and calculation of the electric energy intelligent evaluation record; and meanwhile, an area for accessing the adjusting equipment can be recommended, so that the electric energy quality can be improved in time, the adjusting effect is ensured, the damage of the electric equipment is reduced, comprehensive intelligent monitoring is realized, and the problem of electric energy pollution is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a power quality management monitoring system and method based on the Internet of Things. Background Art

[0002] Power quality is not only related to the safe and economical operation of power grid enterprises, but also affects the safe operation and product quality of users. The power quality monitoring system uses the power quality monitoring terminal installed on the grid side or the user side to transmit the monitoring data back to the monitoring center (monitoring master station or substation) through the network, realizes simultaneous monitoring of multiple locations, and publishes power quality related information. It is an effective means of power quality monitoring and evaluation.

[0003] At present, the way to manage the power quality of the distribution network is mostly by adding some regulating equipment or improving the distribution network lines as a whole. Among them, the management method of connecting new regulating equipment is fast and is the choice of most people. However, which area of ​​the distribution network to add the regulating equipment to and when to add the regulating equipment are both unresolved problems. If the area of ​​the connected regulating equipment is not suitable, it will lead to the failure to achieve the expected regulation effect, and if the regulating equipment is not connected in time, it will cause damage to the electrical equipment. Based on this, this solution provides a power quality management monitoring system and method based on the Internet of Things. Summary of the invention

[0004] The purpose of the present invention is to provide a power quality management monitoring system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a power quality management monitoring method based on the Internet of Things, the monitoring method comprising the following steps: Step S1, obtaining evaluation index data of the monitoring area through the perception layer, and transmitting the evaluation index data to the data layer; Step S2: The monitoring main station center retrieves the evaluation index data, evaluates the power quality of the monitoring area, and obtains the evaluation results, which include normal and abnormal results; Step S3: When the evaluation result is abnormal, obtain the coverage of the regulating equipment that has been connected in the monitoring area and the fault information that has occurred in the monitoring area to determine whether a new regulating equipment needs to be connected; Step S4: If a new regulating device needs to be connected, the recommended area for connecting the regulating device is output.

[0006] Furthermore, the step S1 includes: Divide the power equipment supplied by the same substation into the same monitoring area; Obtaining evaluation index data in the monitoring area through power quality monitoring equipment, wherein the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker; The sensing layer also includes a current sensor, a voltage sensor, a frequency meter and an oscilloscope; The current sensor is used to monitor the current data of the power equipment, and the voltage sensor is used to monitor the voltage data of the power equipment; the frequency meter is used to monitor the frequency of the current and voltage, and the oscilloscope is used to display the waveforms of the current and voltage; Upload the monitored evaluation indicator data to the data layer for storage.

[0007] Furthermore, the step S2 includes: Obtain the threshold range of each evaluation index data, and score each evaluation index data according to the threshold range to form an array [A, B, C, D, E, F], where A is the evaluation index data score of frequency deviation, B is the evaluation index data score of harmonic distortion, C is the evaluation index data score of voltage deviation, D is the evaluation index data score of three-phase imbalance, E is the evaluation index data score of voltage fluctuation, and F is the evaluation index data score of voltage flicker; Draw a radar chart, represent each evaluation indicator data with an axis, and extend all axes from the same point. Mark nodes on the corresponding axis according to the score of each evaluation indicator data, and connect all nodes with lines to form a closed polygon; The calculated area of ​​the polygon is recorded as S1, and the set threshold area is obtained as S2. If the area S1 is greater than the area S2, the evaluation result of the power quality is output as abnormal. If the area S1 is less than the area S2, the evaluation result of the power quality is output as normal.

[0008] Furthermore, the step S3 includes: Step S301: When the evaluation result is abnormal, retrieve the top view of the monitoring area, establish a coordinate system, mark the position of the existing adjustment equipment in the monitoring area on the coordinate system, use it as the reference point, and mark the coverage of the adjustment equipment on the coordinate system. The obtained closed figure is used as the adjustment area, and the remaining area in the monitoring area after the adjustment area is removed is marked as the area to be adjusted; Step S302: taking the interval between two consecutive acquisitions of evaluation results as a marked time period, obtaining fault information occurring in the monitoring area within the marked time period, wherein the fault information includes faulty equipment information and fault index information, wherein the faulty equipment information is information of the faulty power equipment; the fault index information is the fault type and occurrence time, wherein the fault type includes one or more of a fault caused by frequency deviation, a fault caused by waveform distortion, a fault caused by voltage deviation, a fault caused by three-phase imbalance, a fault caused by voltage fluctuation, and a fault caused by voltage flicker; The positions of all power equipment that have experienced failures are marked on the coordinate system and used as secondary reference points. All secondary reference points distributed in the area to be adjusted are extracted as analysis targets. The number of failures that occurred in the power equipment corresponding to the analysis target during the marked time period is obtained, and each failure is marked as a tertiary reference point. The number of tertiary reference points in the adjustment area is counted. At the same time, a new coordinate system is established, with the time when the power equipment corresponding to the third-level reference point begins to fail as the horizontal coordinate, and the duration of the power equipment failure as the vertical coordinate, and the third-level reference points in the adjustment area are marked in the new coordinate system; Step S303, when the current evaluation result is abnormal, the number of the third-level reference points in the area to be adjusted is recorded as Q1; when the previous evaluation result is abnormal, the number of the third-level reference points in the area to be adjusted is recorded as Q2, and the diffusion value between the two evaluation results is calculated. The calculation formula is W=[(Q2-Q1) / (Q1+Q2)] / T, where T represents a numerical value quantified by the time difference between the current evaluation result and the previous evaluation result. When the diffusion value W is greater than the set threshold, the result that requires the connection of a new adjustment device is output; when the diffusion value W is less than the set threshold, the result that does not require the connection of a new adjustment device is output.

[0009] Further, the step S4 includes: Taking the boundary of the adjustment area as the boundary, the unconnected non-adjustment areas are divided and marked as {non-adjustment area 1, non-adjustment area 2, ..., non-adjustment area N}, the third-level reference points corresponding to each non-adjustment area are extracted, the discrete degree of the third-level reference points in each non-adjustment area is calculated, and the non-adjustment area corresponding to the smallest discrete degree is output as the area recommended for access to new adjustment equipment.

[0010] A power quality management and monitoring system based on the Internet of Things is applied to a power quality management and monitoring method based on the Internet of Things. The monitoring system includes: a collection module, an evaluation module, a judgment module and a recommendation module; The acquisition module is used to obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer; The evaluation module is used to monitor the main station center to retrieve evaluation index data, evaluate the power quality of the monitoring area, and obtain evaluation results, which include normal and abnormal; The judgment module is used to obtain the coverage of the regulating equipment that has been connected in the monitoring area and the fault information that has occurred in the monitoring area when the evaluation result is abnormal, and judge whether it is necessary to connect a new regulating equipment; The recommendation module is used to output a recommended area for connecting a new regulating device when a new regulating device needs to be connected.

[0011] Furthermore, the acquisition module includes an evaluation index unit, which is used to obtain evaluation index data in the monitoring area through the power quality monitoring equipment, and the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker.

[0012] Further, the evaluation module includes a quantification unit, a radar scoring unit and a result output unit; The quantification unit is used to obtain the threshold range of each evaluation index data, and score each evaluation index data according to the threshold range; The radar scoring unit is used to draw a radar chart, where each evaluation index data is represented by an axis, all axes extend from the same point, nodes are marked on the corresponding axis according to the score of each evaluation index data, and all nodes are connected by lines to form a closed polygon; The result output unit is used to output the evaluation result of the power quality, and the evaluation result includes normal and abnormal.

[0013] Furthermore, the judgment module includes a region division unit and a calculation unit; The area division unit is used to divide the monitoring area into a regulation area and a non-regulation area; The calculation unit is used to calculate the diffusion value change between two adjacent evaluation results. When the diffusion value W is greater than the set threshold, the result that a new adjustment device needs to be connected is output; when the diffusion value W is less than the set threshold, the result that a new adjustment device does not need to be connected is output.

[0014] Furthermore, the recommendation module is used to divide the unconnected non-adjustment areas based on the boundary of the adjustment area, mark them as {non-adjustment area one, non-adjustment area two, ..., non-adjustment area N}, extract the third-level reference points corresponding to each non-adjustment area, calculate the degree of discreteness of the third-level reference points in each non-adjustment area, and output the non-adjustment area corresponding to the smallest degree of discreteness as the area recommended for access to a new adjustment device.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention can accurately determine whether it is necessary to connect new regulating equipment through analysis and calculation of electric energy intelligent assessment records; and at the same time, it can also recommend the area where the regulating equipment should be connected, so as to improve the power quality in time, ensure the regulating effect, reduce the damage to the electrical equipment, realize all-round intelligent monitoring, and solve the problem of electric energy pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a power quality management and monitoring method based on the Internet of Things of the present invention; Figure 2 This is a schematic diagram of module connections of a power quality management and monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 As shown, the present invention provides a power quality management monitoring method based on the Internet of Things, and the monitoring method comprises the following steps: Step S1, obtaining evaluation index data of the monitoring area through the perception layer, and transmitting the evaluation index data to the data layer; Specifically, the step S1 includes: Divide the power equipment supplied by the same substation into the same monitoring area; Obtaining evaluation index data in the monitoring area through power quality monitoring equipment, wherein the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker; The sensing layer also includes a current sensor, a voltage sensor, a frequency meter and an oscilloscope; The current sensor is used to monitor the current data of the power equipment, and the voltage sensor is used to monitor the voltage data of the power equipment; the frequency meter is used to monitor the frequency of the current and voltage, and the oscilloscope is used to display the waveforms of the current and voltage; Upload the monitored evaluation indicator data to the data layer for storage.

[0019] Step S2: The monitoring main station center retrieves the evaluation index data, evaluates the power quality of the monitoring area, and obtains the evaluation results, which include normal and abnormal results; Specifically, step S2 includes: Obtain the threshold range of each evaluation index data, and score each evaluation index data according to the threshold range to form an array [A, B, C, D, E, F], where A is the evaluation index data score of frequency deviation, B is the evaluation index data score of harmonic distortion, C is the evaluation index data score of voltage deviation, D is the evaluation index data score of three-phase imbalance, E is the evaluation index data score of voltage fluctuation, and F is the evaluation index data score of voltage flicker; Draw a radar chart, represent each evaluation indicator data with an axis, and extend all axes from the same point. Mark nodes on the corresponding axis according to the score of each evaluation indicator data, and connect all nodes with lines to form a closed polygon; The calculated area of ​​the polygon is recorded as S1, and the set threshold area is obtained as S2. If the area S1 is greater than the area S2, the evaluation result of the power quality is output as abnormal. If the area S1 is less than the area S2, the evaluation result of the power quality is output as normal.

[0020] Step S3: When the evaluation result is abnormal, obtain the coverage of the regulating equipment that has been connected in the monitoring area and the fault information that has occurred in the monitoring area to determine whether a new regulating equipment needs to be connected; Specifically, step S3 includes: Step S301: When the evaluation result is abnormal, retrieve the top view of the monitoring area, establish a coordinate system, mark the position of the existing adjustment equipment in the monitoring area on the coordinate system, use it as the reference point, and mark the coverage of the adjustment equipment on the coordinate system. The obtained closed figure is used as the adjustment area, and the remaining area in the monitoring area after the adjustment area is removed is marked as the area to be adjusted; Step S302: taking the interval between two consecutive acquisitions of evaluation results as a marked time period, obtaining fault information occurring in the monitoring area within the marked time period, wherein the fault information includes faulty equipment information and fault index information, wherein the faulty equipment information is information of the faulty power equipment; the fault index information is the fault type and occurrence time, wherein the fault type includes one or more of a fault caused by frequency deviation, a fault caused by waveform distortion, a fault caused by voltage deviation, a fault caused by three-phase imbalance, a fault caused by voltage fluctuation, and a fault caused by voltage flicker; The positions of all power equipment that have experienced failures are marked on the coordinate system and used as secondary reference points. All secondary reference points distributed in the area to be adjusted are extracted as analysis targets. The number of failures that occurred in the power equipment corresponding to the analysis target during the marked time period is obtained, and each failure is marked as a tertiary reference point. The number of tertiary reference points in the adjustment area is counted. At the same time, a new coordinate system is established, with the time when the power equipment corresponding to the third-level reference point begins to fail as the horizontal coordinate, and the duration of the power equipment failure as the vertical coordinate, and the third-level reference points in the adjustment area are marked in the new coordinate system; Step S303, when the current evaluation result is abnormal, the number of the third-level reference points in the area to be adjusted is recorded as Q1; when the previous evaluation result is abnormal, the number of the third-level reference points in the area to be adjusted is recorded as Q2, and the diffusion value between the two evaluation results is calculated. The calculation formula is W=[(Q2-Q1) / (Q1+Q2)] / T, where T represents a numerical value quantified by the time difference between the current evaluation result and the previous evaluation result. When the diffusion value W is greater than the set threshold, the result that requires the connection of a new adjustment device is output; when the diffusion value W is less than the set threshold, the result that does not require the connection of a new adjustment device is output.

[0021] Step S4: If a new regulating device needs to be connected, the recommended area for connecting the regulating device is output.

[0022] Specifically, step S4 includes: Taking the boundary of the adjustment area as the boundary, the unconnected non-adjustment area is divided and marked as {non-adjustment area 1, non-adjustment area 2, ..., non-adjustment area N}, the third-level reference points corresponding to each non-adjustment area are extracted, the discrete degree of the third-level reference points in each non-adjustment area is calculated, and the non-adjustment area corresponding to the minimum discrete degree is output as the area recommended for access to the new adjustment device; Specifically, the dispersion degree of the three-level benchmark points can be calculated using the mean difference, and the calculation formula is: ; in It is expressed as the degree of discreteness, Q represents the number of third-level reference points corresponding to a certain non-adjustment area, Represents each data point, Represents the mean of the data.

[0023] Embodiment 2: Figure 2 As shown, a power quality management and monitoring system based on the Internet of Things is applied to a power quality management and monitoring method based on the Internet of Things. The monitoring system includes: a collection module, an evaluation module, a judgment module and a recommendation module; The acquisition module is used to obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer; Specifically, the acquisition module includes an evaluation index unit, which is used to obtain evaluation index data in the monitoring area through power quality monitoring equipment, and the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker.

[0024] The evaluation module is used to monitor the main station center to retrieve evaluation index data, evaluate the power quality of the monitoring area, and obtain evaluation results, which include normal and abnormal; Specifically, the evaluation module includes a quantification unit, a radar scoring unit and a result output unit; The quantification unit is used to obtain the threshold range of each evaluation index data, and score each evaluation index data according to the threshold range; The radar scoring unit is used to draw a radar chart, where each evaluation index data is represented by an axis, all axes extend from the same point, nodes are marked on the corresponding axis according to the score of each evaluation index data, and all nodes are connected by lines to form a closed polygon; The result output unit is used to output the evaluation result of the power quality, and the evaluation result includes normal and abnormal.

[0025] The judgment module is used to obtain the coverage of the regulating equipment that has been connected in the monitoring area and the fault information that has occurred in the monitoring area when the evaluation result is abnormal, and judge whether it is necessary to connect a new regulating equipment; Specifically, the judgment module includes a region division unit and a calculation unit; The area division unit is used to divide the monitoring area into a regulation area and a non-regulation area; The calculation unit is used to calculate the diffusion value change between two adjacent evaluation results. When the diffusion value W is greater than the set threshold, the result that a new adjustment device needs to be connected is output; when the diffusion value W is less than the set threshold, the result that a new adjustment device does not need to be connected is output.

[0026] The recommendation module is used to output a recommended area for connecting a new regulating device when a new regulating device needs to be connected.

[0027] Specifically, the recommendation module is used to divide the unconnected non-adjustment areas based on the boundary of the adjustment area, mark them as {non-adjustment area 1, non-adjustment area 2, ..., non-adjustment area N}, extract the third-level reference points corresponding to each non-adjustment area, calculate the degree of discreteness of the third-level reference points in each non-adjustment area, and output the non-adjustment area corresponding to the smallest degree of discreteness as the area recommended for access to a new adjustment device.

[0028] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for monitoring power quality based on the Internet of Things, characterized in that: The monitoring method comprises the following steps: Step S1, obtaining evaluation index data of the monitoring area through the perception layer, and transmitting the evaluation index data to the data layer; Step S2: The monitoring main station center retrieves the evaluation index data, evaluates the power quality of the monitoring area, and obtains the evaluation results, which include normal and abnormal results; Step S3: When the evaluation result is abnormal, obtain the coverage of the regulating equipment that has been connected in the monitoring area and the fault information that has occurred in the monitoring area to determine whether a new regulating equipment needs to be connected; Step S4: If a new regulating device needs to be connected, the recommended area for connecting the regulating device is output.

2. According to the method for monitoring power quality based on the Internet of Things in claim 1, it is characterized by: The step S1 comprises: Divide the power equipment supplied by the same substation into the same monitoring area; Obtaining evaluation index data in the monitoring area through power quality monitoring equipment, wherein the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker; The sensing layer also includes a current sensor, a voltage sensor, a frequency meter and an oscilloscope; The current sensor is used to monitor the current data of the power equipment, and the voltage sensor is used to monitor the voltage data of the power equipment; the frequency meter is used to monitor the frequency of the current and voltage, and the oscilloscope is used to display the waveforms of the current and voltage; Upload the monitored evaluation indicator data to the data layer for storage.

3. According to the method for monitoring power quality based on the Internet of Things in claim 1, it is characterized by: The step S2 comprises: Obtain the threshold range of each evaluation index data, and score each evaluation index data according to the threshold range to form an array [A, B, C, D, E, F], where A is the evaluation index data score of frequency deviation, B is the evaluation index data score of harmonic distortion, C is the evaluation index data score of voltage deviation, D is the evaluation index data score of three-phase imbalance, E is the evaluation index data score of voltage fluctuation, and F is the evaluation index data score of voltage flicker; Draw a radar chart, represent each evaluation indicator data with an axis, and extend all axes from the same point. Mark nodes on the corresponding axis according to the score of each evaluation indicator data, and connect all nodes with lines to form a closed polygon; The calculated area of ​​the polygon is recorded as S1, and the set threshold area is obtained as S2. If the area S1 is greater than the area S2, the evaluation result of the power quality is output as abnormal. If the area S1 is less than the area S2, the evaluation result of the power quality is output as normal.

4. The method for monitoring power quality based on the Internet of Things according to claim 1 is characterized in that: The step S3 comprises: Step S301: When the evaluation result is abnormal, retrieve the top view of the monitoring area, establish a coordinate system, mark the position of the existing adjustment equipment in the monitoring area on the coordinate system, use it as the reference point, and mark the coverage of the adjustment equipment on the coordinate system. The obtained closed figure is used as the adjustment area, and the remaining area in the monitoring area after the adjustment area is removed is marked as the area to be adjusted; Step S302: taking the interval between two consecutive acquisitions of evaluation results as a marked time period, obtaining fault information occurring in the monitoring area within the marked time period, wherein the fault information includes faulty equipment information and fault index information, wherein the faulty equipment information is information of the faulty power equipment; the fault index information is the fault type and occurrence time, wherein the fault type includes one or more of a fault caused by frequency deviation, a fault caused by waveform distortion, a fault caused by voltage deviation, a fault caused by three-phase imbalance, a fault caused by voltage fluctuation, and a fault caused by voltage flicker; The positions of all power equipment that have experienced failures are marked on the coordinate system and used as secondary reference points. All secondary reference points distributed in the area to be adjusted are extracted as analysis targets. The number of failures that occurred in the power equipment corresponding to the analysis target during the marked time period is obtained, and each failure is marked as a tertiary reference point. The number of tertiary reference points in the adjustment area is counted. At the same time, a new coordinate system is established, with the time when the power equipment corresponding to the third-level reference point begins to fail as the horizontal coordinate, and the duration of the power equipment failure as the vertical coordinate, and the third-level reference points in the adjustment area are marked in the new coordinate system; Step S303, when the current evaluation result is abnormal, the number of the third-level reference points in the area to be adjusted is recorded as Q1; when the previous evaluation result is abnormal, the number of the third-level reference points in the area to be adjusted is recorded as Q2, and the diffusion value between the two evaluation results is calculated. The calculation formula is W=[(Q2-Q1) / (Q1+Q2)] / T, where T represents a numerical value quantified by the time difference between the current evaluation result and the previous evaluation result. When the diffusion value W is greater than the set threshold, the result that requires the connection of a new adjustment device is output; when the diffusion value W is less than the set threshold, the result that does not require the connection of a new adjustment device is output.

5. The method for monitoring power quality based on the Internet of Things according to claim 1 is characterized in that: The step S4 comprises: Taking the boundary of the adjustment area as the boundary, the unconnected non-adjustment areas are divided and marked as {non-adjustment area 1, non-adjustment area 2, ..., non-adjustment area N}, the third-level reference points corresponding to each non-adjustment area are extracted, the discrete degree of the third-level reference points in each non-adjustment area is calculated, and the non-adjustment area corresponding to the smallest discrete degree is output as the area recommended for access to new adjustment equipment.

6. A power quality management and monitoring system based on the Internet of Things, which is applied to a power quality management and monitoring method based on the Internet of Things as described in any one of claims 1 to 5, characterized in that: The monitoring system includes: a collection module, an evaluation module, a judgment module and a recommendation module; The acquisition module is used to obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer; The evaluation module is used to monitor the main station center to retrieve evaluation index data, evaluate the power quality of the monitoring area, and obtain evaluation results, which include normal and abnormal; The judgment module is used to obtain the coverage of the regulating equipment that has been connected in the monitoring area and the fault information that has occurred in the monitoring area when the evaluation result is abnormal, and judge whether it is necessary to connect a new regulating equipment; The recommendation module is used to output a recommended area for connecting a new regulating device when a new regulating device needs to be connected.

7. The power quality management and monitoring system based on the Internet of Things according to claim 6 is characterized by: The acquisition module includes an evaluation index unit, which is used to obtain evaluation index data in the monitoring area through power quality monitoring equipment, and the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker.

8. The power quality management and monitoring system based on the Internet of Things according to claim 6 is characterized by: The evaluation module includes a quantification unit, a radar scoring unit and a result output unit; The quantification unit is used to obtain the threshold range of each evaluation index data, and score each evaluation index data according to the threshold range; The radar scoring unit is used to draw a radar chart, where each evaluation index data is represented by an axis, all axes extend from the same point, nodes are marked on the corresponding axis according to the score of each evaluation index data, and all nodes are connected by lines to form a closed polygon; The result output unit is used to output the evaluation result of the power quality, and the evaluation result includes normal and abnormal.

9. The power quality management and monitoring system based on the Internet of Things according to claim 6 is characterized by: The judgment module includes a region division unit and a calculation unit; The area division unit is used to divide the monitoring area into a regulation area and a non-regulation area; The calculation unit is used to calculate the diffusion value change between two adjacent evaluation results. When the diffusion value W is greater than the set threshold, the result that a new adjustment device needs to be connected is output; when the diffusion value W is less than the set threshold, the result that a new adjustment device does not need to be connected is output.

10. The power quality management and monitoring system based on the Internet of Things according to claim 6 is characterized by: The recommendation module is used to divide the unconnected non-adjustment areas based on the boundary of the adjustment area, mark them as {non-adjustment area 1, non-adjustment area 2, ..., non-adjustment area N}, extract the third-level reference points corresponding to each non-adjustment area, calculate the degree of discreteness of the third-level reference points in each non-adjustment area, and output the non-adjustment area corresponding to the smallest degree of discreteness as the area recommended for access to a new adjustment device.

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