A visual power distribution room environment monitoring method, system and device

Abstract

The present invention discloses a visualized distribution room environment monitoring method, system and device, including a data acquisition unit, a database, a data sorting unit, an early warning analysis unit and a visualization unit, and the present invention relates to the field of environmental monitoring technology. The visualized distribution room environment monitoring method, system and device can determine the abnormal attribute set corresponding to the fault characteristics of different equipment by comparing and analyzing the historical data under normal and abnormal conditions. This method can effectively identify the key parameters that cause equipment failures and provide a scientific basis for fault diagnosis and prevention; the early warning analysis unit can perform abnormal analysis on the current real-time collected environmental data according to the results of the sorting and analysis, and timely predict and warn of possible equipment failures. This helps operation and maintenance personnel take preventive measures to avoid or reduce the risk of failures; the intuitive information display method facilitates operation and maintenance personnel to quickly understand the current status of the distribution room and improve response efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a visualized power distribution room environment monitoring method, system and device. Background Art

[0002] In the field of power distribution room environment monitoring, traditional monitoring methods often rely on manual inspections and regular maintenance, which is not only inefficient but also difficult to achieve real-time monitoring and early warning. As the power system develops towards intelligence and automation, higher requirements are placed on power distribution room environment monitoring technology. The existing power distribution room environment monitoring system has the following problems:

[0003] Limitations of manual inspection: In the past, environmental monitoring of power distribution rooms mainly relied on staff to conduct regular on-site inspections and use handheld instruments to read environmental parameters such as temperature, humidity, and harmful gas concentrations. This method is time-consuming and labor-intensive, and cannot achieve real-time monitoring. It has a slow response speed to sudden environmental changes or equipment failures, making it difficult to discover and handle problems in a timely manner.

[0004] Insufficient data collection and analysis: Traditional monitoring methods usually lack systematic data collection and analysis capabilities. Since data records mostly rely on paper forms or personal records, it is not easy to store, retrieve and analyze data for a long time, resulting in low utilization of historical data and difficulty in mining potential problems and failure modes from the data.

[0005] Lack of early warning system: Traditional environmental monitoring systems often lack effective early warning mechanisms. Even if some more advanced systems can issue alarms, most of these alarms are based on simple logical judgments of a single parameter threshold, without comprehensive consideration of the correlation between different environmental parameters, and cannot accurately predict complex fault conditions.

[0006] Difficulty in fault diagnosis: When a fault occurs, traditional monitoring methods cannot quickly and accurately diagnose the cause of the fault, and usually require shutdown for maintenance. Possible fault points must be manually checked one by one. This trial-and-error method is time-consuming and affects the stable operation of the power system.

[0007] Diversity and complexity of environmental parameters: There are many different environmental parameters in the power distribution room, including temperature, humidity, smoke, concentration of harmful gases, etc. Different parameters have different effects on equipment operation and require comprehensive monitoring and analysis. Traditional monitoring methods often cannot fully cover all key parameters, resulting in blind spots in monitoring.

[0008] Increased demand for intelligence and automation: As the power industry develops towards intelligence and automation, higher requirements are placed on the intelligence and automation level of distribution room environmental monitoring technology. Modern power systems require a comprehensive solution that can achieve real-time monitoring, data analysis, fault warning and intelligent decision-making.

[0009] In view of the deficiencies and challenges in the above background technology, the present invention provides a visual distribution room environment monitoring method, system and device, which aims to achieve real-time monitoring of distribution room environmental parameters, historical data analysis, abnormal warning and fault diagnosis through advanced sensor technology, data analysis methods and visualization means, so as to improve the operation safety and operation and maintenance efficiency of the distribution room. Summary of the invention

[0010] In view of the deficiencies in the prior art, the present invention provides a visualized method, system and device for monitoring the environment of a power distribution room, which solve the problems raised in the background technology.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: a visual power distribution room environment monitoring system, comprising:

[0012] A data collection unit is used to collect environmental data in the power distribution room in real time by deploying a variety of sensors in the power distribution room;

[0013] A database for storing historical environmental data of all power distribution facilities in the power distribution room under normal and abnormal conditions;

[0014] A data sorting unit, used to sort and analyze normal environment data and abnormal environment data based on historical environment data under normal conditions and under abnormal conditions;

[0015] The early warning analysis unit is used to perform abnormal analysis on the environmental data currently collected in the power distribution room in real time according to the analysis results of the data sorting unit, and obtain the corresponding fault signal, which is then transmitted to the visualization unit:

[0016] The visualization unit is used to display the data parameters of different attributes in the environmental data in the distribution room in real time. It is also used to display the corresponding fault signals obtained by the early warning analysis unit on the visualization interface to notify the operation and maintenance personnel.

[0017] As a further solution of the present invention: environmental data are data parameters of different attributes detected by sensors deployed at key locations in the distribution room, such as temperature parameters, humidity parameters, smoke parameters, harmful gas concentration parameters, water level parameters, current parameters and voltage parameters.

[0018] As a further solution of the present invention: the analysis method is as follows:

[0019] The first step is to obtain normal environmental data through historical environmental data under normal conditions;

[0020] The second step is to select a time node corresponding to a device failure feature from multiple equipment failure features in the distribution room as the abnormal time node, and then obtain several standard nodes before the abnormal time node according to the preset interval length, and then obtain data parameters of different attributes on each standard node, and then perform abnormal analysis on the data parameters of each different attribute on all standard nodes, and obtain the abnormal parameter set corresponding to each attribute, and then perform correlation analysis on the abnormal parameter set of all attributes corresponding to each device failure feature, and obtain the abnormal attribute set of the corresponding equipment failure feature.

[0021] As a further solution of the present invention: the specific method of the first step is as follows:

[0022] StepA1. In a target cycle, set a number of analysis nodes with the same time intervals in the cycle;

[0023] Step A2, obtain all historical environmental data in normal state on each analysis node, and then mark the data parameters with the same attribute inside it as Ci, i = 1, 2, ... n, the number of data parameters with the same attribute is the number of analysis nodes;

[0024] StepA3, through the formula Calculate the discrete value CL of the data parameter corresponding to the attribute at each analysis node, where Cp represents the average value of all Ci involved in the discrete value calculation;

[0025] At the same time, through the formula Calculate the mean difference XC between the data parameters corresponding to the attribute on adjacent analysis nodes, where i≠n;

[0026] StepA4, then compare CL with Ly, where Ly is the preset discrete threshold:

[0027] If CL<Ly holds, Cp is used as the normal parameter standard value ZC of the corresponding attribute in the target period, and As the normal parameter standard interval of the corresponding attribute;

[0028] On the contrary, when CL≥Ly holds, the corresponding Ci values ​​are deleted in descending order of |Ci-Cp| until CL<Ly holds;

[0029] Then extract all the Ci values ​​that have not been deleted, and extract the data parameters adjacent to the analysis node from all the Ci values ​​that have not been deleted, and use C i -C i-1 Calculate the difference, then obtain the average value XC1 of the difference by taking the data parameters on all adjacent analysis nodes;

[0030] At the same time, its average value is used as the normal parameter standard value ZC of the corresponding attribute in the period, and then As the normal parameter standard interval of the corresponding attribute;

[0031] Step A5, and so on, calculate the normal parameter standard value and normal parameter standard range of other attributes;

[0032] Among them, the normal parameter standard value and normal parameter standard range of each attribute are the normal environmental data.

[0033] As a further solution of the present invention: the abnormality analysis method in the second step is as follows:

[0034] Select the data parameters on all standard nodes of the same attribute:

[0035] Extract the time difference between each standard node and the abnormal time node, and obtain several data parameters on the corresponding standard node in order from small to large, and compare them with their corresponding normal parameter standard intervals in turn:

[0036] When there are g0 consecutive data parameters in the normal parameter standard interval, the data parameters with a larger time difference between the standard node and the abnormal time node are no longer compared with the normal parameter standard interval, and all data parameters before the corresponding standard node of the data parameters that are g0 consecutively in the normal parameter standard interval are recorded as the abnormal parameter set of the attribute, which contains several abnormal parameter values;

[0037] And so on, get the exception parameter sets corresponding to other attributes.

[0038] As a further solution of the present invention: the correlation analysis method in the second step is as follows:

[0039] First, the number of abnormal parameter values ​​in the abnormal parameter set corresponding to each attribute is obtained respectively, and marked as YCj, j = 1, 2, ... m;

[0040] Then, by Calculate the proportion of abnormal parameter values ​​corresponding to each attribute YBj;

[0041] Then, the number ratio YBj of abnormal parameter values ​​corresponding to each attribute is compared with the preset ratio threshold YBy:

[0042] When YBj ≥ YBy of an attribute, it means that the abnormal parameter value corresponding to the attribute is the key parameter in the fault characteristics of the device;

[0043] When YBj≥YBy of multiple attributes, it means that there is a correlation relationship between the multiple attributes;

[0044] Then, the multiple attributes in a correlation relationship are used as an abnormal attribute set of the fault characteristics of the device;

[0045] In this way, the abnormal attribute sets of all different equipment failure characteristics are calculated.

[0046] As a further solution of the present invention: the specific method of the early warning analysis unit is as follows:

[0047] First, at the current time node, the environmental data in the power distribution room is collected in real time, and the data parameters of different attributes in the environmental data are compared with the normal parameter standard range corresponding to the attribute:

[0048] Then, all attributes whose data parameters are not within the normal parameter standard range are obtained, and the attributes are recorded as abnormal attributes;

[0049] Then all the abnormal attributes are matched with multiple attributes in multiple abnormal attribute sets respectively:

[0050] If the similarity rate between all abnormal attributes and multiple attributes in one abnormal attribute set reaches k value, it means that there is a device fault feature corresponding to the abnormal attribute set at the current time node, and a known fault signal is generated;

[0051] If the similarity rate between all abnormal attributes and multiple attributes in one abnormal attribute set does not reach the k value, it means that there is an unknown equipment fault feature at the current time node, and an unknown fault signal is generated;

[0052] Where k is a preset percentage coefficient, wherein k is 96.54%;

[0053] When an unknown fault signal is generated, the environmental data in the power distribution room is collected in real time at the next time node with a specified interval from the current time node, and the analysis is continued through the above steps;

[0054] As a further solution of the present invention: wherein, when comparing the data parameters of different attributes in the collected environment data with the normal parameter standard intervals corresponding to the attributes,

[0055] If there are no data parameters that are not within the normal parameter standard range among all the attributes, no corresponding fault signal is generated, and it indicates that the operating status of the distribution room at the corresponding collection time node is normal.

[0056] A visualized power distribution room environment monitoring method is implemented by the above-mentioned visualized power distribution room environment monitoring system.

[0057] A visual power distribution room environment monitoring device, comprising a sensor group, a central processing unit and a touch display, wherein the sensor group, the central processing unit and the touch display are communicatively connected, and the device is used to implement the above-mentioned visual power distribution room environment monitoring system;

[0058] The sensor group is used to detect and obtain data parameters of different attributes, and the sensor group includes a temperature sensor, a humidity sensor, a smoke sensor, a harmful gas concentration sensor, a water level sensor, and a current and voltage sensor;

[0059] The central processing unit includes a data acquisition unit, a database, a data sorting unit and an early warning analysis unit;

[0060] The touch display comprises a visualization unit.

[0061] The present invention provides a visual distribution room environment monitoring method, system and device. Compared with the prior art, it has the following beneficial effects:

[0062] The beneficial effects, advantages and strengths of the present invention are mainly reflected in the following aspects:

[0063] Real-time monitoring and data collection: By deploying a variety of sensors in the power distribution room, environmental data can be collected in real time, including temperature, humidity, smoke, concentration of harmful gases, water level, current and voltage, etc. This real-time monitoring mechanism is conducive to timely detection of potential problems in the power distribution room and ensure the safe and stable operation of the power distribution system.

[0064] Historical data analysis and standard establishment: The system stores and analyzes historical environmental data of the power distribution room under normal and abnormal conditions, thereby establishing standards for normal and abnormal environmental data. This helps provide an accurate reference benchmark for subsequent early warning analysis and fault diagnosis.

[0065] Determination of abnormal attribute sets: By comparing and analyzing historical data under normal and abnormal conditions, the abnormal attribute sets corresponding to different equipment failure characteristics can be determined. This method can effectively identify the key parameters that cause equipment failures and provide a scientific basis for fault diagnosis and prevention.

[0066] Early warning analysis and fault prediction: The early warning analysis unit can analyze the abnormalities of the environmental data currently collected in real time based on the results of the collation and analysis, and timely predict and warn of possible equipment failures. This helps operation and maintenance personnel take preventive measures to avoid or reduce the risk of failures.

[0067] Visualization: The visualization unit provided by the system can display the environmental data in the power distribution room in real time and intuitively display the early warning analysis results on the interface. This intuitive information display method makes it easier for operation and maintenance personnel to quickly understand the current status of the power distribution room and improve response efficiency.

[0068] Adaptive adjustment and optimization: During the analysis process, the system can adaptively adjust the normal parameter standard range to adapt to changes in the distribution room environment. This adaptive capability makes the system more flexible and accurate, and can better adapt to different operating conditions.

[0069] Fault signal generation and processing: When the system detects abnormal properties, it will generate known or unknown fault signals according to the preset matching rules. For unknown faults, the system will continue to analyze at subsequent time nodes in order to discover new fault modes and continuously improve the fault database.

[0070] Improving the safety of distribution rooms: Overall, the invention can improve the safety and reliability of distribution rooms, reduce equipment failures caused by environmental factors, reduce operation and maintenance costs, and ensure the stability of power supply.

[0071] In summary, the present invention provides a comprehensive and automated solution for distribution room environment monitoring by integrating functions such as data monitoring, historical data analysis, abnormal attribute set determination, early warning analysis, and visual display, which significantly improves the management level and fault response capability of distribution rooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a system block diagram of the present invention;

[0073] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] Embodiment 1

[0076] See also Figure 1-Figure 2 The present invention provides a technical solution: a visual distribution room environment monitoring system, comprising:

[0077] A data collection unit is used to collect environmental data in the power distribution room in real time by deploying a variety of sensors in the power distribution room;

[0078] Environmental data refers to data parameters of different attributes detected by sensors deployed at key locations in the power distribution room, such as temperature parameters, humidity parameters, smoke parameters, harmful gas concentration parameters, water level parameters, current parameters, and voltage parameters;

[0079] A database for storing historical environmental data of all power distribution facilities in the power distribution room under normal and abnormal conditions;

[0080] A data collating unit, used for obtaining normal environment data through historical environment data under normal conditions;

[0081] StepA1. In a target cycle, set a number of analysis nodes with the same time intervals in the cycle;

[0082] Step A2, obtain all historical environmental data in normal state on each analysis node, and then mark the data parameters with the same attribute inside it as Ci, i = 1, 2, ... n, the number of data parameters with the same attribute is the number of analysis nodes;

[0083] StepA3, through the formula Calculate the discrete value CL of the data parameter corresponding to the attribute at each analysis node, where Cp represents the average value of all Ci involved in the discrete value calculation;

[0084] At the same time, through the formula Calculate the mean difference XC between the data parameters corresponding to the attribute on adjacent analysis nodes, where i≠n;

[0085] StepA4, then compare CL with Ly, where Ly is the preset discrete threshold:

[0086] If CL<Ly holds, Cp is used as the normal parameter standard value ZC of the corresponding attribute in the target period, and As the normal parameter standard interval of the corresponding attribute;

[0087] On the contrary, when CL≥Ly holds, the corresponding Ci values ​​are deleted in descending order of |Ci-Cp| until CL<Ly holds;

[0088] Then extract all the Ci values ​​that have not been deleted, and extract the data parameters adjacent to the analysis node from all the Ci values ​​that have not been deleted, and use C i -C i-1 Calculate the difference, then obtain the average value XC1 of the difference by taking the data parameters on all adjacent analysis nodes;

[0089] At the same time, its average value is used as the normal parameter standard value ZC of the corresponding attribute in the period, and then As the normal parameter standard interval of the corresponding attribute;

[0090] Step A5, and so on, calculate the normal parameter standard value and normal parameter standard range of other attributes;

[0091] Among them, the normal parameter standard value and normal parameter standard range of each attribute are normal environmental data;

[0092] The early warning analysis unit is used to perform abnormal analysis on the environmental data currently collected in the power distribution room in real time based on the analysis results of the data sorting unit:

[0093] The specific method is as follows:

[0094] First, at the current time node, the environmental data in the power distribution room is collected in real time, and the data parameters of different attributes in the environmental data are compared with the normal parameter standard range corresponding to the attribute:

[0095] Then, all attributes whose data parameters are not within the normal parameter standard range are obtained, and the attributes are recorded as abnormal attributes;

[0096] Among them, the data parameters of different attributes in the corresponding collected environment data are compared with the normal parameter standard range corresponding to the attribute:

[0097] If there are no data parameters that are not in the normal parameter standard range among all the attributes, no corresponding fault signal is generated, and it indicates that the operation status of the power distribution room at the corresponding collection time node is normal;

[0098] This embodiment uses a variety of sensors to collect environmental data in the distribution room in real time, such as temperature, humidity, smoke, etc. The system can monitor potential problems in a timely manner and issue early warnings, which greatly improves the efficiency and accuracy of monitoring compared to traditional manual inspections. It uses historical data to establish standard environmental parameters, which provides an objective benchmark for fault detection and helps operation and maintenance personnel make more accurate maintenance and adjustment decisions based on data. By comparing real-time data with preset normal parameter standard ranges, the system can quickly identify abnormal attributes, accelerate the fault diagnosis process, and reduce equipment downtime. It can promptly detect situations that may pose a threat to personnel safety, such as excessive concentrations of harmful gases, and take measures in advance to protect the safety of personnel and equipment.

[0099] Embodiment 2

[0100] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that,

[0101] The data sorting unit is also used to obtain the normal environment data obtained by combining the historical environment data under the normal state and the historical environment data under the abnormal state to obtain the abnormal attribute set;

[0102] Step V1. From the multiple equipment failure characteristics of the power distribution room, a time node corresponding to the equipment failure characteristic is selected as the abnormal time node. Then, according to the preset interval length, several standard nodes are obtained before the abnormal time node. Then, data parameters of different attributes are obtained at each standard node, and then the data parameters of each different attribute at all standard nodes are analyzed for abnormality.

[0103] The exception analysis method is as follows:

[0104] Take the data parameters on all standard nodes of the same attribute as an example:

[0105] Extract the time difference between each standard node and the abnormal time node, and obtain several data parameters on the corresponding standard node in order from small to large, and compare them with their corresponding normal parameter standard intervals in turn:

[0106] When there are g0 consecutive data parameters in the normal parameter standard interval, the data parameters with a larger time difference between the standard node and the abnormal time node are no longer compared with the normal parameter standard interval, and all data parameters before the corresponding standard node of the data parameters that are g0 consecutively in the normal parameter standard interval are recorded as the abnormal parameter set of the attribute, which contains several abnormal parameter values;

[0107] And so on, get the abnormal parameter sets corresponding to other attributes;

[0108] Step V2: Perform correlation analysis on the abnormal parameter sets of all attributes corresponding to a device fault feature:

[0109] The correlation analysis method is as follows:

[0110] First, the number of abnormal parameter values ​​in the abnormal parameter set corresponding to each attribute is obtained respectively, and marked as YCj, j = 1, 2, ... m;

[0111] Then, by Calculate the proportion of abnormal parameter values ​​corresponding to each attribute YBj;

[0112] Then, the number ratio YBj of abnormal parameter values ​​corresponding to each attribute is compared with the preset ratio threshold YBy:

[0113] When YBj ≥ YBy of an attribute, it means that the abnormal parameter value corresponding to the attribute is the key parameter in the fault characteristics of the device;

[0114] When YBj≥YBy of multiple attributes, it means that there is a correlation relationship between the multiple attributes;

[0115] Then, the multiple attributes in a correlation relationship are used as an abnormal attribute set of the fault characteristics of the device;

[0116] In this way, the abnormal attribute set of all different equipment failure characteristics is calculated;

[0117] The early warning analysis unit is also used to match all abnormal attributes with multiple attributes in multiple abnormal attribute sets respectively:

[0118] If the similarity rate between all abnormal attributes and multiple attributes in one abnormal attribute set reaches k value, it means that there is a device fault feature corresponding to the abnormal attribute set at the current time node, and a known fault signal is generated;

[0119] If the similarity rate between all abnormal attributes and multiple attributes in one abnormal attribute set does not reach the k value, it means that there is an unknown equipment fault feature at the current time node, and an unknown fault signal is generated;

[0120] Wherein k is a preset percentage coefficient. In this embodiment, k is 96.54%;

[0121] When an unknown fault signal is generated, the environmental data in the power distribution room is collected in real time at the next time node with a specified interval from the current time node, and the analysis is continued through the above steps;

[0122] This embodiment introduces the concept of abnormal attribute set. By analyzing historical fault data, the system can identify parameter change patterns related to specific fault characteristics and improve the accuracy of fault prediction. Through correlation analysis, the connection between different attributes can be discovered, which helps to understand the causes of complex faults and achieve more in-depth fault analysis and prevention. Known and unknown fault signals can be generated according to the matched abnormal attributes and the preset matching rate (k value), which helps to take corresponding maintenance measures in time or further investigate the cause of unknown faults.

[0123] Embodiment 3

[0124] As the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments, and the difference between the technical solution of this embodiment and the first and second embodiments is that this embodiment also includes:

[0125] The visualization unit is used to display the data parameters of different attributes in the environmental data in the power distribution room in real time. It is also used to display the corresponding abnormal attributes and fault signals obtained by the early warning analysis unit on the visualization interface to notify the operation and maintenance personnel:

[0126] This embodiment displays real-time data and warning information on a visual interface, so that operation and maintenance personnel can intuitively understand the operating status of the distribution room, improve monitoring efficiency and response speed; combined with the technical solutions of embodiment one and embodiment two, a more comprehensive monitoring system is provided, which can not only monitor and warn in real time, but also provide in-depth fault diagnosis through historical data analysis.

[0127] Embodiment 4

[0128] As the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second and third embodiments.

[0129] This embodiment integrates the technical solutions of the previous three embodiments to construct a comprehensive system that integrates real-time monitoring, historical data analysis, fault pattern recognition and information visualization, providing strong technical support for efficient, safe and intelligent management of distribution rooms; it realizes a high degree of automation and intelligence from data collection to fault warning, fault diagnosis and information presentation, greatly improving the operation and maintenance efficiency and the reliability of distribution rooms; relying on the system's intelligent analysis capabilities, operation and maintenance resources can be allocated more specifically, optimizing maintenance work and reducing operation and maintenance costs.

[0130] In summary, these embodiments demonstrate a progressively deeper and more comprehensive approach to improve the monitoring and management level of power distribution rooms and ensure the stable operation of the power system.

[0131] The present invention also provides a technical solution: a visual distribution room environment monitoring method, which is implemented by the above-mentioned visual distribution room environment monitoring system:

[0132] The method comprises the following steps:

[0133] Step 1: Data Collection

[0134] By deploying a variety of sensors in the power distribution room, the environmental data in the power distribution room can be collected in real time. These data include parameters such as temperature, humidity, smoke, concentration of harmful gases, water level, current and voltage.

[0135] Step 2: Data storage

[0136] The collected environmental data is stored in a database, which stores the historical environmental data of all power distribution facilities in the power distribution room under normal and abnormal conditions.

[0137] Step 3: Data collation and analysis

[0138] The standard value and standard range of normal environmental data are derived from historical data.

[0139] Perform anomaly analysis on the current real-time collected environmental data and identify any abnormal attributes by comparing the current data with the normal parameter standard range.

[0140] Step 4: Abnormal attribute set analysis

[0141] Combined with historical fault data, abnormal attribute sets are analyzed and established, which helps to predict and diagnose faults more accurately.

[0142] Step 5: Information matching and fault signal generation

[0143] Match all abnormal attributes with the known abnormal attribute set, and generate known or unknown fault signals according to the preset matching rate (k value);

[0144] If no abnormality is detected, it means that the distribution room is operating normally.

[0145] Step 6: Visual display: The environmental data in the power distribution room is displayed in real time through the visualization unit, and the early warning analysis results and fault signals are displayed on the visualization interface so that the operation and maintenance personnel can respond in time.

[0146] This method provides strong technical support for efficient, safe and intelligent management of power distribution rooms through real-time monitoring, historical data analysis, fault pattern recognition and information visualization. This not only improves monitoring efficiency and response speed, but also optimizes maintenance work through intelligent analysis, reduces operation and maintenance costs, and ensures the stable operation of the power system.

[0147] The present invention also provides a technical solution: a visual power distribution room environment monitoring device, comprising a sensor group, a central processing unit and a touch display, wherein the sensor group, the central processing unit and the touch display are communicatively connected, and the device is used to implement the above-mentioned visual power distribution room environment monitoring system;

[0148] The sensor group is used to detect and obtain data parameters of different attributes. The sensor group includes a temperature sensor, a humidity sensor, a smoke sensor, a harmful gas concentration sensor, a water level sensor, and a current and voltage sensor;

[0149] The central processing unit includes a data acquisition unit, a database, a data sorting unit and an early warning analysis unit;

[0150] The touch display contains the visualization unit.

[0151] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0152] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0153] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A visual distribution room environment monitoring system, characterized in that: include: A data collection unit is used to collect environmental data in the power distribution room in real time by deploying a variety of sensors in the power distribution room; A database for storing historical environmental data of all power distribution facilities in the power distribution room under normal and abnormal conditions; A data sorting unit, used to sort and analyze normal environment data and abnormal environment data based on historical environment data under normal conditions and under abnormal conditions; The analysis is organized as follows: The first step is to obtain normal environmental data through historical environmental data under normal conditions; The second step is to select a time node corresponding to a device failure feature from multiple device failure features in the power distribution room as an abnormal time node, and then obtain a number of standard nodes before the abnormal time node according to a preset interval, and then obtain data parameters of different attributes on each standard node, and then perform abnormal analysis on the data parameters of each different attribute on all standard nodes, and obtain the abnormal parameter set corresponding to each attribute, and then perform correlation analysis on the abnormal parameter set of all attributes corresponding to each device failure feature, and obtain the abnormal attribute set of the corresponding device failure feature; The abnormal analysis method in the second step is as follows: Select the data parameters on all standard nodes of the same attribute: Extract the time difference between each standard node and the abnormal time node, and obtain several data parameters on the corresponding standard node in order from small to large, and compare them with their corresponding normal parameter standard intervals in turn: When there are g0 consecutive data parameters in the normal parameter standard interval, the data parameters with a larger time difference between the standard node and the abnormal time node are no longer compared with the normal parameter standard interval, and all data parameters before the corresponding standard node of the data parameters that are g0 consecutively in the normal parameter standard interval are recorded as the abnormal parameter set of the attribute, which contains several abnormal parameter values; And so on, get the abnormal parameter sets corresponding to other attributes; The correlation analysis method in the second step is as follows: First, obtain the number of abnormal parameter values ​​in the abnormal parameter set corresponding to each attribute and mark them as YCj, j=1, 2, ...m; Then, by , ,…… Calculate the proportion of abnormal parameter values ​​corresponding to each attribute YBj; Then, the number ratio YBj of abnormal parameter values ​​corresponding to each attribute is compared with the preset ratio threshold YBy: When YBj ≥ YBy of an attribute, it means that the abnormal parameter value corresponding to the attribute is the key parameter in the fault characteristics of the device; When YBj≥YBy of multiple attributes, it means that there is a correlation relationship between the multiple attributes; Then, the multiple attributes in a correlation relationship are used as an abnormal attribute set of the fault characteristics of the device; In this way, the abnormal attribute set of all different equipment failure characteristics is calculated; The early warning analysis unit is used to perform abnormal analysis on the environmental data currently collected in the power distribution room in real time according to the analysis results of the data sorting unit, and obtain the corresponding fault signal, which is then transmitted to the visualization unit; The specific method of the early warning analysis unit is as follows: First, at the current time node, the environmental data in the power distribution room is collected in real time, and the data parameters of different attributes in the environmental data are compared with the normal parameter standard range corresponding to the attribute: Then, all attributes whose data parameters are not within the normal parameter standard range are obtained, and the attributes are recorded as abnormal attributes; Then all the abnormal attributes are matched with multiple attributes in multiple abnormal attribute sets respectively: If the similarity rate between all abnormal attributes and multiple attributes in one abnormal attribute set reaches k value, it means that there is a device fault feature corresponding to the abnormal attribute set at the current time node, and a known fault signal is generated; If the similarity rate between all abnormal attributes and multiple attributes in one abnormal attribute set does not reach the k value, it means that there is an unknown equipment fault feature at the current time node, and an unknown fault signal is generated; Where k is the preset percentage coefficient, and the value of k is 96.54%; When an unknown fault signal is generated, the environmental data in the power distribution room is collected in real time at the next time node with a specified interval from the current time node, and the analysis is continued through the above steps; The visualization unit is used to display the data parameters of different attributes in the environmental data in the distribution room in real time. It is also used to display the corresponding fault signals obtained by the early warning analysis unit on the visualization interface to notify the operation and maintenance personnel.

2. A visual power distribution room environment monitoring system according to claim 1, characterized in that: Environmental data refers to data parameters of different attributes detected by sensors deployed at key locations in the power distribution room. The data parameters of different attributes include temperature parameters, humidity parameters, smoke parameters, harmful gas concentration parameters, water level parameters, current parameters, and voltage parameters.

3. A visual power distribution room environment monitoring system according to claim 2, characterized in that: The specific method in the first step is as follows: StepA1. In a target cycle, set a number of analysis nodes with the same time intervals in the cycle; StepA2, obtain all historical environmental data in normal state on each analysis node, and then mark the data parameters with the same attribute inside it as Ci, i=1, 2, ... n, the number of data parameters with the same attribute is the number of analysis nodes; StepA3, through the formula , calculate the discrete value CL of the data parameter corresponding to the attribute on each analysis node, where Cp represents the average value of all Ci involved in the discrete value calculation; At the same time, through the formula Calculate the mean difference XC between the data parameters corresponding to the attribute on adjacent analysis nodes, where i≠n; StepA4, then compare CL with Ly, where Ly is the preset discrete threshold: If CL<Ly holds, Cp is used as the normal parameter standard value ZC of the corresponding attribute in the target period, and As the normal parameter standard interval of the corresponding attribute; On the contrary, when CL≥Ly holds, the corresponding Ci values ​​are deleted in descending order of |Ci-Cp| until CL<Ly holds; Then extract all the Ci values ​​that have not been deleted, and extract the data parameters adjacent to the analysis node from all the Ci values ​​that have not been deleted, and use Calculate the difference, then obtain the average value XC1 of the difference by taking the data parameters on all adjacent analysis nodes; At the same time, its average value is used as the normal parameter standard value ZC of the corresponding attribute in the period, and then As the normal parameter standard interval of the corresponding attribute; Step A5, and so on, calculate the normal parameter standard value and normal parameter standard range of other attributes; Among them, the normal parameter standard value and normal parameter standard range of each attribute are the normal environmental data.

4. The visual power distribution room environment monitoring system according to claim 1 is characterized in that: in, When comparing the data parameters of different attributes in the corresponding collected environment data with the normal parameter standard range corresponding to the attribute, If there are no data parameters that are not within the normal parameter standard range among all the attributes, no corresponding fault signal is generated, and it indicates that the operating status of the distribution room at the corresponding collection time node is normal.

5. A visual distribution room environment monitoring method, characterized in that: The method is implemented by a visualized power distribution room environment monitoring system as described in any one of claims 1-4.

6. A visual power distribution room environment monitoring device, characterized in that: The device comprises a sensor group, a central processing unit and a touch display, and the sensor group, the central processing unit and the touch display are communicatively connected, and is used to implement a visual distribution room environment monitoring system as described in any one of claims 1 to 4; The sensor group is used to detect and obtain data parameters of different attributes, and the sensor group includes a temperature sensor, a humidity sensor, a smoke sensor, a harmful gas concentration sensor, a water level sensor, and a current and voltage sensor; The central processing unit includes a data acquisition unit, a database, a data sorting unit and an early warning analysis unit; The touch display comprises a visualization unit.

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