Intelligent power distribution station multi-terminal equipment management system and method

Abstract

The invention relates to the technical field of power distribution station equipment management, and discloses an intelligent power distribution station multi-terminal equipment management system and method. The system comprises a data acquisition unit, a data transmission unit, a central processing unit, a user interface unit and an equipment management unit. According to the invention, by eliminating abnormal values, the data accuracy and the equipment state evaluation precision are improved; multi-feature comprehensive evaluation avoids one-sidedness of single-parameter evaluation, and the actual state of the equipment is reflected more comprehensively and accurately; secondly, information quantification of maintenance personnel is scientific and comprehensive, resources are reasonably arranged according to priority classification, and equipment maintenance efficiency and quality are improved; a visual management interface is convenient for a user to check an analysis result, and the participation degree and the decision-making efficiency are improved; finally, the system realizes automatic closed-loop management, improves equipment management efficiency, reduces manual intervention and errors, reduces cost, and provides powerful guarantee for safe and stable operation of the power distribution station.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution station equipment management, and in particular to a smart distribution station multi-terminal equipment management system and method. Background Art

[0002] With the continuous development of power systems and the advancement of intelligentization, distribution stations play a vital role in power supply. Distribution stations contain many terminal devices, and the stable operation of these devices is directly related to the reliability and safety of the power system. However, the traditional distribution station equipment management method has many problems and is difficult to meet the needs of modern power systems.

[0003] Traditional equipment management mainly relies on manual inspections and regular maintenance, which is inefficient and difficult to grasp the operating status of equipment in real time. Moreover, manual inspections are prone to missed inspections and false inspections, and are unable to detect potential equipment failures in a timely manner. In addition, traditional equipment management methods lack in-depth analysis and processing of equipment operation data, making it difficult to accurately assess the status of equipment and predict the occurrence of failures. For the maintenance and management of terminal equipment in distribution stations, the traditional method is usually to arrange maintenance personnel based on experience, which lacks a scientific basis. There are differences in the skill levels of different maintenance personnel. How to reasonably allocate maintenance tasks to ensure that equipment can be maintained in a timely and effective manner is an urgent problem to be solved.

[0004] Therefore, in order to solve the problems existing in the traditional distribution station equipment management and improve the reliability of equipment operation and the efficiency of maintenance management, a smart distribution station multi-terminal equipment management system and method is urgently needed. Summary of the invention

[0005] The purpose of the present invention is to provide a smart distribution station multi-terminal equipment management system and method, which solves the technical problems raised in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A smart distribution station multi-terminal equipment management system, comprising:

[0008] A data acquisition unit is used to collect the operating data of each terminal device in the distribution station, including but not limited to operating parameters corresponding to multiple characteristics such as voltage, current, power, temperature, humidity, and vibration;

[0009] A data transmission unit, used to transmit the data collected by the data collection unit to the central processing unit;

[0010] The central processing unit is used to perform data preprocessing, feature extraction processing, and comprehensive evaluation and analysis on the received data in sequence, and obtain the status evaluation index of the terminal device, and then perform device abnormality diagnosis based on the status evaluation index of the terminal device, and determine the status of the terminal device;

[0011] The user interface unit is used to display the analysis results obtained by the central processing unit to the user.

[0012] As a further solution of the present invention: the data preprocessing method is as follows:

[0013] Step 1.1, select an operating parameter corresponding to a feature in a terminal device as an example;

[0014] Delimit a specified period and divide a number of extraction nodes within the specified period;

[0015] Step 1.2: Extract the operating parameters of each extraction node of the feature in the terminal device within the specified period and mark it as Y i , i = 1, 2, ... n, n represents the number of extracted nodes;

[0016] Step 1.3, calculate all Y i The average value of , and mark it as YP;

[0017] Calculate all Y i The standard deviation of , and label it as YX;

[0018] Step 1.4, then combine YP and YX, from all Y i Determine abnormal parameters in

[0019] The specific method is as follows:

[0020] If |Y i -YP|>u×YX, then the Y i It is determined to be an abnormal parameter, and then all Y i Remove the Y i ;

[0021] If |Y i -YP|≤u×YX, then Y i It is judged as an abnormal parameter and is not included in all Y i Remove the Y i ;

[0022] Wherein, u is a preset quantitative factor. In this embodiment, u takes a value of 3, and outliers are eliminated by the Laida criterion;

[0023] Step 1.5. Get all the Yi that have not been eliminated and re-label them as Tj , j = 1, 2, ... m, m represents the number of operating parameters not eliminated in the n extraction nodes, and m ≤ n.

[0024] As a further solution of the present invention: the feature extraction processing method is as follows:

[0025] Step 2.1, obtain the normal range of each feature corresponding to the terminal device;

[0026] Then, a feature of the terminal device is selected as an example, and the normal range of the feature is extracted. min ,T max ];

[0027] Step 2.2, in the feature corresponding to the terminal device, obtain the unremoved operating parameter T j ;

[0028] Step 2.3, through Calculate the parameter deviation value P of the feature;

[0029] Step 2.4, through Calculate the feature score D of the feature;

[0030] Step 2.5: Calculate the feature score corresponding to each feature in the terminal device according to the method from Step 2.1 to Step 2.4.

[0031] As a further solution of the present invention: the comprehensive evaluation and analysis method is as follows:

[0032] Step 3.1. Extract the feature score values ​​corresponding to each feature in the terminal device and mark them as D t , t = 1, 2, ... e, e represents the number of each feature;

[0033] Step 3.2, through Calculate the status evaluation index Z of the terminal device;

[0034] In the formula, α t It is the preset weight value corresponding to each feature.

[0035] As a further solution of the present invention: the device abnormality diagnosis method is as follows

[0036] Step 4.1, perform data preprocessing, feature extraction processing, comprehensive evaluation and analysis on the data received by each terminal device, and obtain the status evaluation index of each terminal device;

[0037] Step 4.2: Mark the status evaluation index of each terminal device as Z g, g = 1, 2, ... v, v represents the number of each terminal device;

[0038] Then, the status evaluation index of each terminal device is combined with a preset evaluation judgment threshold set to perform status judgment;

[0039] Among them, the evaluation decision threshold set is [60, 80].

[0040] As a further solution of the present invention: in Step 4.2, the state determination method is as follows:

[0041] If Z g <60, the g-th terminal device is judged to be in a bad state;

[0042] If 60≤Z g <80, it is determined that the g-th terminal device is in a general state;

[0043] If Z g ≥80, the g-th terminal device is judged to be in good condition;

[0044] As a further solution of the present invention: the data acquisition unit is also used to collect maintenance personnel information used for operation and maintenance of each terminal device in the power distribution station;

[0045] Maintenance personnel information includes:

[0046] The qualification level coefficient of the relevant maintenance personnel, where the qualification level coefficient is mainly measured by the professionally certified skill level certificate and the time it took to obtain the skill level certificate, and the skill level certificate is also assigned a value;

[0047] Among them, the skill level certificates are divided into three levels: primary, intermediate and senior, and the primary skill level certificate is assigned a value of 0.3, the junior skill level certificate is assigned a value of 0.6, and the junior skill level certificate is assigned a value of 0.9;

[0048] Among them, the qualification level coefficient = skill level certificate value × k1 + skill level certificate acquisition time × k2, where k1 and k2 are the corresponding preset proportion coefficients respectively;

[0049] The working years coefficient of the relevant maintenance personnel, where the working years coefficient is the main measurement indicator based on the number of years the maintenance personnel have worked in the relevant work;

[0050] Among them, working years coefficient = working years × k3, where k3 is the corresponding preset proportion coefficient;

[0051] The work experience coefficient of the relevant maintenance personnel, where the work experience coefficient is based on the number of fault problems and types of fault problems that the maintenance personnel have handled in the relevant work as the main measurement indicators;

[0052] Among them, work experience coefficient = number of fault problems × k4 + (number of fault problem types / number of fault problems) × k5, where k4 and k5 are the corresponding preset proportion coefficients respectively;

[0053] The central processing unit is also used to calculate the skill level index of each maintenance personnel based on the maintenance personnel information;

[0054] As a further solution of the present invention: the skill level index is obtained in the following manner:

[0055] The skill level index of each maintenance personnel is obtained through R = U1 × β1 + U2 × β2 + U3 × β3

[0056] In the formula, R is the skill level index of the maintenance personnel, U1 is the qualification level coefficient of the maintenance personnel, U2 is the working years coefficient of the maintenance personnel, and U3 is the working experience coefficient of the maintenance personnel;

[0057] The central processing unit is also used to classify the skill level indicators of each maintenance personnel in combination with a preset skill determination threshold set for priority classification;

[0058] Among them, the skill judgment threshold set is [12, 18]

[0059] The priorities are categorized as follows:

[0060] If R < 12, the relevant maintenance personnel will be designated as high-priority maintenance objects;

[0061] If 12≤R<18, the relevant maintenance personnel will be designated as medium priority maintenance objects;

[0062] If R≥18, the relevant maintenance personnel will be designated as low-priority maintenance objects.

[0063] As a further solution of the present invention: also include:

[0064] The equipment management unit is used to maintain and manage the terminal equipment according to the analysis results of the central processing unit;

[0065] The specific method is as follows:

[0066] For terminal equipment in poor condition with status evaluation index Zg<60, maintenance personnel corresponding to high-priority maintenance objects shall be arranged to inspect and repair them;

[0067] For general terminal equipment with status evaluation index 60≤Zg<80, maintenance personnel corresponding to medium priority maintenance objects shall be arranged to inspect and repair them;

[0068] For terminal equipment in good condition with a condition evaluation index Zg≥80, maintenance personnel corresponding to low-priority maintenance objects are arranged to inspect and repair them.

[0069] A method for managing multi-terminal equipment in a smart power distribution station is implemented by the multi-terminal equipment management system of a smart power distribution station, and the method comprises the following steps:

[0070] Step 1: Data Collection

[0071] The data acquisition unit collects the operating data of each terminal device in the distribution station, including but not limited to the operating parameters corresponding to multiple characteristics such as voltage, current, power, temperature, humidity, and vibration, and collects the maintenance personnel information used for the operation and maintenance of each terminal device in the distribution station, including but not limited to the qualification level coefficient, working years coefficient, and working experience coefficient;

[0072] Step 2: Data Transmission

[0073] The data collected by the data collection unit is transmitted to the central processing unit by using the data transmission unit;

[0074] Step 3: Comprehensive processing

[0075] The received data is sequentially subjected to data preprocessing, feature extraction processing, and comprehensive evaluation and analysis, and the status evaluation index of the terminal device is obtained. Then, the device abnormality diagnosis is performed based on the status evaluation index of the terminal device, and the status of the terminal device is determined;

[0076] Step 31: Data preprocessing

[0077] For the operating parameters corresponding to a feature in a terminal device, define a specified period and divide the extraction nodes, extract the operating parameters of each node and mark them, calculate the average and standard deviation, eliminate abnormal parameters according to the Laida criterion, obtain the parameters that are not eliminated and re-mark them;

[0078] Step 3.2: Feature extraction and processing

[0079] Get the normal range of each feature of the terminal device, take one feature as an example, get the uneliminated operating parameters, calculate the parameter deviation value and feature score value, and repeat this operation for each feature.

[0080] Step 3: Comprehensive evaluation and analysis

[0081] Extract the feature score value corresponding to each feature of the terminal device and calculate the status evaluation index of the terminal device;

[0082] Step 34: Equipment abnormality diagnosis

[0083] The data received by each terminal device is processed as above to obtain a status evaluation index, and the status is determined by combining the preset evaluation threshold set to be classified into bad, general, and good status;

[0084] Step 4: Calculate skill level indicators and priority classification

[0085] The skill level index is calculated based on the maintenance personnel information, and the priority is classified into high, medium and low priority maintenance objects based on the preset skill judgment threshold set.

[0086] Step 5: Results display

[0087] The analysis result obtained by the central processing unit is displayed to the user through the user interface unit.

[0088] Step 6: Device Management

[0089] According to the analysis results of the central processing unit, the equipment management unit performs maintenance and management on the terminal equipment. Terminal equipment in poor condition is repaired by high-priority maintenance personnel, terminal equipment in general condition is repaired by medium-priority maintenance personnel, and terminal equipment in good condition is repaired by low-priority maintenance personnel.

[0090] Beneficial effects of the present invention:

[0091] Comprehensive data collection: The data collection unit collects multi-feature operating data of each terminal device in the distribution station, including voltage, current, power, temperature, humidity, vibration, etc., which can fully reflect the operating status of the equipment and provide a rich data basis for accurately evaluating the equipment status.

[0092] Data preprocessing improves accuracy: The Raida criterion is used to remove outliers to ensure that the data used in subsequent analysis is more accurate and reliable. By calculating the mean and standard deviation to determine abnormal parameters, noise and outliers in the data can be effectively removed, improving the accuracy of equipment status assessment.

[0093] Multi-feature comprehensive evaluation: After data preprocessing and feature extraction, the parameter deviation value and feature score value of each feature are combined to calculate the status evaluation index of the terminal device. This multi-feature comprehensive evaluation method can more comprehensively and accurately reflect the actual operating status of the device and avoid the one-sidedness of single parameter evaluation.

[0094] Quantification of maintenance personnel information: Quantifying the qualification level coefficient, working years coefficient and work experience coefficient of maintenance personnel can objectively reflect the professional ability and experience level of maintenance personnel. The qualification level coefficient takes into account the skill level certificate and the time of obtaining the certificate, the working years coefficient takes the working years as an indicator, and the work experience coefficient combines the number and type of fault problems handled, making the evaluation of maintenance personnel more scientific and comprehensive.

[0095] Priority classification to arrange resources reasonably: Priority classification is performed based on the skill level indicators of maintenance personnel combined with the preset skill judgment threshold set, and maintenance personnel are divided into high, medium and low priority maintenance objects. This classification method can better match terminal equipment in different states with suitable maintenance personnel, improve the efficiency and quality of equipment maintenance, and achieve reasonable allocation of resources.

[0096] Visual management interface: The user interface unit provides users with a visual equipment management interface, which allows users to view the analysis results obtained by the central processing unit. This enables users to intuitively understand the equipment operation status and maintenance personnel information, improving user participation in equipment management and decision-making efficiency.

[0097] Efficient equipment management: The entire system realizes the automated management of multi-terminal equipment in the distribution station, forming a complete closed-loop management process from data collection, transmission, analysis to equipment maintenance arrangements. This greatly improves the efficiency of equipment management, reduces manual intervention and errors, and reduces management costs.

[0098] In summary, the smart distribution station multi-terminal equipment management system and method can improve the accuracy of equipment status assessment, optimize maintenance personnel management, improve user experience and management efficiency, and provide strong guarantees for the safe and stable operation of distribution stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The present invention will be further described below in conjunction with the accompanying drawings.

[0100] Figure 1 It is a system block diagram of a smart distribution station multi-terminal equipment management system and method of the present invention.

[0101] Figure 2 It is a flow chart of a central processing unit in a smart distribution station multi-terminal equipment management system and method of the present invention. DETAILED DESCRIPTION

[0102] 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.

[0103] Embodiment 1

[0104] See also Figure 1 and Figure 2 As shown, the present invention is a smart distribution station multi-terminal equipment management system and method, including:

[0105] A data acquisition unit is used to collect the operating data of each terminal device in the distribution station, including but not limited to operating parameters corresponding to multiple characteristics such as voltage, current, power, temperature, humidity, and vibration;

[0106] A data transmission unit, used to transmit the data collected by the data collection unit to the central processing unit;

[0107] The central processing unit is used to analyze and process the received data and use specific algorithms to evaluate the equipment status and diagnose faults;

[0108] The specific method is as follows:

[0109] Step 1: Data preprocessing

[0110] Step 1.1, select an operating parameter corresponding to a feature in a terminal device as an example;

[0111] Delimit a specified period and divide a number of extraction nodes within the specified period;

[0112] Step 1.2: Extract the operating parameters of each extraction node of the feature in the terminal device within the specified period and mark it as Y i , i = 1, 2, ... n, n represents the number of extracted nodes;

[0113] Step 1.3, calculate all Y i The average value of , and mark it as YP;

[0114] Calculate all Y i The standard deviation of , and label it as YX;

[0115] Step 1.4, then combine YP and YX, from all Y i Determine abnormal parameters in

[0116] The specific method is as follows:

[0117] If |Y i -YP|>u×YX, then the Y i It is determined to be an abnormal parameter, and then all Y i Remove the Y i ;

[0118] If |Y i -YP|≤u×YX, then Y i It is judged as an abnormal parameter and is not included in all Y i Remove the Y i ;

[0119] Wherein, u is a preset quantitative factor. In this embodiment, u takes a value of 3, and outliers are eliminated by the Laida criterion;

[0120] Step 1.5. Get all the Yi that have not been eliminated and re-label them as T j , j = 1, 2, ... m, m represents the number of operating parameters not eliminated in the n extraction nodes, and m ≤ n;

[0121] Step 2: Feature extraction processing

[0122] Step 2.1, obtain the normal range of each feature corresponding to the terminal device;

[0123] Then, a feature of the terminal device is selected as an example, and the normal range of the feature is extracted. min ,T max ];

[0124] Step 2.2, in the feature corresponding to the terminal device, obtain the unremoved operating parameter T j ;

[0125] Step 2.3, through Calculate the parameter deviation value P of the feature;

[0126] Step 2.4, through Calculate the feature score D of the feature;

[0127] Step 2.5, according to the method of Step 2.1 to Step 2.4, calculate the feature score value corresponding to each feature in the terminal device;

[0128] Step 3: Comprehensive evaluation and analysis

[0129] Step 3.1. Extract the feature score value corresponding to each feature in the terminal device and mark it as D t , t = 1, 2, ... e, e represents the number of each feature;

[0130] Step 3.2, through Calculate the status evaluation index Z of the terminal device;

[0131] In the formula, α t is the preset weight value corresponding to each feature;

[0132] Step 4: Equipment abnormality diagnosis

[0133] Step 4.1. Calculate the status evaluation index of each terminal device according to the method from the first step to the third step;

[0134] Step 4.2: Mark the status evaluation index of each terminal device as Z g , g = 1, 2, ... v, v represents the number of each terminal device;

[0135] Then, the status evaluation index of each terminal device is combined with a preset evaluation judgment threshold set to perform status judgment;

[0136] Among them, the evaluation threshold set is [60, 80]

[0137] The status is determined as follows:

[0138] If Z g <60, the g-th terminal device is judged to be in a bad state;

[0139] If 60≤Z g <80, it is determined that the g-th terminal device is in a general state;

[0140] If Z g ≥80, the g-th terminal device is judged to be in good condition;

[0141] The user interface unit is used to provide users with a visual device management interface, so that users can easily view the analysis results obtained by the central processing unit.

[0142] This embodiment uses a data acquisition unit to comprehensively collect multi-feature operating data of each terminal device in the distribution station, including voltage, current, power, temperature, humidity, vibration and other parameters, which provides a rich data basis for equipment status evaluation and fault diagnosis, and can more comprehensively understand the operating status of the equipment; adopts a specific data preprocessing method and uses the Laida criterion to perform outlier removal processing, which can effectively remove outliers in the data, improve the accuracy and reliability of the data, and provide purer data for subsequent analysis and processing; in the feature extraction process, the parameter deviation value and feature score value of each feature are calculated, which can quantify the performance of different features of the equipment, which helps to more accurately evaluate the equipment status; in the comprehensive evaluation and analysis, by calculating the status evaluation index of the terminal equipment and combining the preset weight values ​​of each feature, the influence of different features on the equipment status can be comprehensively considered, making the evaluation result more scientific and reasonable; in the equipment abnormality diagnosis link, the status is judged according to the status evaluation index and the preset evaluation judgment threshold set, which can quickly and accurately determine whether the status of the equipment is bad, general or good, and provide clear guidance for the maintenance and management of the equipment; the user interface unit provides users with a visual equipment management interface, which is convenient for users to view the analysis results obtained by the central processing unit, improves the convenience and transparency of equipment management, and enables users to timely understand the operating status and maintenance needs of the equipment.

[0143] Embodiment 2

[0144] 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,

[0145] The data collection unit is also used to collect maintenance personnel information used for operation and maintenance of each terminal equipment in the power distribution station;

[0146] Maintenance personnel information includes:

[0147] The qualification level coefficient of the relevant maintenance personnel, where the qualification level coefficient is mainly measured by the professionally certified skill level certificate and the time it took to obtain the skill level certificate, and the skill level certificate is also assigned a value;

[0148] Among them, the skill level certificates are divided into three levels: primary, intermediate and senior, and the primary skill level certificate is assigned a value of 0.3, the junior skill level certificate is assigned a value of 0.6, and the junior skill level certificate is assigned a value of 0.9;

[0149] Among them, the qualification level coefficient = skill level certificate value × k1 + skill level certificate acquisition time × k2, where k1 and k2 are the corresponding preset proportion coefficients respectively;

[0150] The working years coefficient of the relevant maintenance personnel, where the working years coefficient is the main measurement indicator based on the number of years the maintenance personnel have worked in the relevant work;

[0151] Among them, working years coefficient = working years × k3, where k3 is the corresponding preset proportion coefficient;

[0152] The work experience coefficient of the relevant maintenance personnel, where the work experience coefficient is based on the number of fault problems and types of fault problems that the maintenance personnel have handled in the relevant work as the main measurement indicators;

[0153] Among them, work experience coefficient = number of fault problems × k4 + (number of fault problem types / number of fault problems) × k5, where k4 and k5 are the corresponding preset proportion coefficients respectively;

[0154] The central processing unit is also used to calculate the skill level index of each maintenance personnel based on the maintenance personnel information;

[0155] The specific method is:

[0156] R=U1×β1+U2×β2+U3×β3;

[0157] In the formula, R is the skill level index of the maintenance personnel, U1 is the qualification level coefficient of the maintenance personnel, U2 is the working years coefficient of the maintenance personnel, and U3 is the working experience coefficient of the maintenance personnel;

[0158] The central processing unit is also used to classify the skill level indicators of each maintenance personnel in combination with a preset skill determination threshold set for priority classification;

[0159] Among them, the skill judgment threshold set is [12, 18]

[0160] The priorities are categorized as follows:

[0161] If R < 12, the relevant maintenance personnel will be designated as high-priority maintenance objects;

[0162] If 12≤R<18, the relevant maintenance personnel will be designated as medium priority maintenance objects;

[0163] If R ≥ 18, the relevant maintenance personnel will be designated as low-priority maintenance objects;

[0164] Also includes:

[0165] The equipment management unit is used to maintain and manage the terminal equipment according to the analysis results of the central processing unit;

[0166] The specific method is as follows:

[0167] For the state evaluation index Z g For terminal equipment with a bad status of less than 60%, maintenance personnel corresponding to high-priority maintenance objects will be arranged to inspect and repair them;

[0168] For the state evaluation index 60≤Z g For general status terminal equipment with a value less than 80, maintenance personnel corresponding to the medium-priority maintenance object shall be arranged to inspect and repair it;

[0169] For the state evaluation index Z g For terminal equipment in good condition with a value of ≥80, maintenance personnel corresponding to low-priority maintenance objects will be arranged to inspect and repair them.

[0170] In this embodiment, the data acquisition unit not only collects equipment operation data, but also collects maintenance personnel information used for operation and maintenance of each terminal equipment in the distribution station, including multiple aspects such as qualification level coefficient, working years coefficient and work experience coefficient, providing a comprehensive basis for the skill level evaluation of maintenance personnel; the qualification level coefficient of maintenance personnel is quantitatively evaluated, with professionally certified skill level certificates and the length of time they have been obtained as the main measurement indicators, and skill certificates of different levels are assigned values, which can objectively reflect the professional skill level of maintenance personnel; the calculation method of the working years coefficient and the work experience coefficient uses the working years and the number and type of fault problems handled as the main measurement indicators, respectively, which can comprehensively consider the work experience of maintenance personnel and their actual problem-solving capabilities; the central processing unit calculates the skill level indicators of each maintenance personnel based on the maintenance personnel information, and adopts a comprehensive calculation formula, combining the qualification level coefficient, working years coefficient and work experience coefficient. The coefficient can comprehensively and accurately evaluate the skill level of maintenance personnel; the skill level indicators of maintenance personnel are combined with the preset skill judgment threshold set for priority classification, and are divided into high, medium and low priority maintenance objects, providing a more targeted personnel arrangement plan for equipment maintenance management; the equipment management unit maintains and manages the terminal equipment according to the analysis results of the central processing unit, and arranges maintenance personnel of different priorities to carry out maintenance for terminal equipment in different states, which can improve the efficiency and quality of equipment maintenance; high-priority maintenance objects are arranged for maintenance of terminal equipment in poor state, which can handle equipment failures in time and reduce the impact of equipment failures on the operation of the distribution station; medium-priority maintenance objects are arranged for maintenance of terminal equipment in general state, which can reasonably allocate maintenance resources; low-priority maintenance objects are arranged for maintenance of terminal equipment in good state, which can make full use of the resources of maintenance personnel and keep the equipment in good operating condition.

[0171] Embodiment 3

[0172] As the third embodiment of the present invention, when the present application is specifically 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 mentioned above for implementation.

[0173] This embodiment combines the solutions of embodiment one and embodiment two for implementation, giving full play to the advantages of the two embodiments and realizing comprehensive management of the terminal equipment of the distribution station; in terms of data collection and processing, it can not only collect multi-feature operation data of the equipment for accurate status evaluation and fault diagnosis, but also collect maintenance personnel information for skill level evaluation and priority classification, providing more comprehensive support for equipment maintenance management; in terms of equipment maintenance management, it can reasonably arrange maintenance personnel for maintenance according to the equipment status evaluation results and the priority classification of maintenance personnel, thereby improving the efficiency and quality of equipment maintenance, and at the same time, it can also make better use of maintenance resources and reduce equipment maintenance costs.

[0174] The present invention also provides a method for managing multi-terminal equipment in a smart power distribution station. The method is implemented by the smart power distribution station multi-terminal equipment management system, and the method includes the following steps:

[0175] Step 1: Data Collection

[0176] The data acquisition unit collects the operating data of each terminal device in the distribution station, including but not limited to the operating parameters corresponding to multiple characteristics such as voltage, current, power, temperature, humidity, and vibration, and collects the maintenance personnel information used for the operation and maintenance of each terminal device in the distribution station, including but not limited to the qualification level coefficient, working years coefficient, and working experience coefficient;

[0177] Step 2: Data Transmission

[0178] The data collected by the data collection unit is transmitted to the central processing unit by using the data transmission unit;

[0179] Step 3: Comprehensive processing

[0180] The received data is sequentially subjected to data preprocessing, feature extraction processing, and comprehensive evaluation and analysis, and the status evaluation index of the terminal device is obtained. Then, the device abnormality diagnosis is performed based on the status evaluation index of the terminal device, and the status of the terminal device is determined;

[0181] Step 31: Data preprocessing

[0182] For the operating parameters corresponding to a feature in a terminal device, define a specified period and divide the extraction nodes, extract the operating parameters of each node and mark them, calculate the average and standard deviation, eliminate abnormal parameters according to the Laida criterion, obtain the parameters that are not eliminated and re-mark them;

[0183] Step 3.2: Feature extraction and processing

[0184] Get the normal range of each feature of the terminal device, take one feature as an example, get the uneliminated operating parameters, calculate the parameter deviation value and feature score value, and repeat this operation for each feature.

[0185] Step 3: Comprehensive evaluation and analysis

[0186] Extract the feature score value corresponding to each feature of the terminal device and calculate the status evaluation index of the terminal device;

[0187] Step 34: Equipment abnormality diagnosis

[0188] The data received by each terminal device is processed as above to obtain a status evaluation index, and the status is determined by combining the preset evaluation threshold set to be classified into bad, general, and good status;

[0189] Step 4: Calculate skill level indicators and priority classification

[0190] The skill level index is calculated based on the maintenance personnel information, and the priority is classified into high, medium and low priority maintenance objects based on the preset skill judgment threshold set.

[0191] Step 5: Results display

[0192] The analysis result obtained by the central processing unit is displayed to the user through the user interface unit.

[0193] Step 6: Device Management

[0194] According to the analysis results of the central processing unit, the equipment management unit performs maintenance and management on the terminal equipment. Terminal equipment in poor condition is repaired by high-priority maintenance personnel, terminal equipment in general condition is repaired by medium-priority maintenance personnel, and terminal equipment in good condition is repaired by low-priority maintenance personnel.

[0195] 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.

[0196] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A smart distribution station multi-terminal equipment management system, characterized in that: include: A data acquisition unit is used to collect the operating data of each terminal device in the distribution station, including but not limited to operating parameters corresponding to multiple characteristics such as voltage, current, power, temperature, humidity, and vibration; A data transmission unit, used to transmit the data collected by the data collection unit to the central processing unit; The central processing unit is used to perform data preprocessing, feature extraction processing, and comprehensive evaluation and analysis on the received data in sequence, and obtain the status evaluation index of the terminal device, and then perform device abnormality diagnosis based on the status evaluation index of the terminal device, and determine the status of the terminal device; The user interface unit is used to display the analysis results obtained by the central processing unit to the user.

2. According to claim 1, a smart distribution station multi-terminal equipment management system is characterized in that: The data preprocessing method is as follows: Step 1.1, select an operating parameter corresponding to a feature in a terminal device as an example; Delimit a specified period and divide a number of extraction nodes within the specified period; Step 1.2: Extract the operating parameters of each extraction node of the feature in the terminal device within the specified period and mark it as Y i , i = 1, 2, ... n, n represents the number of extracted nodes; Step 1.3, calculate all Y i The average value of , and mark it as YP; Calculate all Y i The standard deviation of , and label it as YX; Step 1.4, then combine YP and YX, from all Y i Determine abnormal parameters in The specific method is as follows: If |Y i -YP|>u×YX, then the Y i It is determined to be an abnormal parameter, and then all Y i Remove the Y i ; If |Y i -YP|≤u×YX, then Y i It is judged as an abnormal parameter and is not included in all Y i Remove the Y i ; Wherein, u is a preset quantitative factor. In this embodiment, u takes a value of 3, and outliers are eliminated by the Laida criterion; Step 1.

5. Get all the Yi that have not been eliminated and re-label them as T j , j = 1, 2, ... m, m represents the number of operating parameters not eliminated in the n extraction nodes, and m ≤ n.

3. A smart distribution station multi-terminal equipment management system according to claim 2, characterized in that: The feature extraction process is as follows: Step 2.1, obtain the normal range of each feature corresponding to the terminal device; Then, a feature of the terminal device is selected as an example, and the normal range of the feature is extracted. min ,T max ]; Step 2.2, in the feature corresponding to the terminal device, obtain the unremoved operating parameter T j ; Step 2.3, through Calculate the parameter deviation value P of the feature; Step 2.4, pass Calculate the feature score D of the feature; Step 2.5: Calculate the feature score corresponding to each feature in the terminal device according to the method from Step 2.1 to Step 2.

4.

4. A smart distribution station multi-terminal equipment management system according to claim 3, characterized in that: The comprehensive evaluation and analysis method is as follows: Step 3.

1. Extract the feature score values ​​corresponding to each feature in the terminal device and mark them as D t , t = 1, 2, ... e, e represents the number of each feature; Step 3.2, through Calculate the status evaluation index Z of the terminal device; In the formula, α t It is the preset weight value corresponding to each feature.

5. A smart distribution station multi-terminal equipment management system according to claim 4, characterized in that: The device abnormality diagnosis method is as follows Step 4.1, perform data preprocessing, feature extraction processing, comprehensive evaluation and analysis on the data received by each terminal device, and obtain the status evaluation index of each terminal device; Step 4.2, mark the status evaluation index of each terminal device as Zg, g = 1, 2, ... v, v represents the number of each terminal device; Then, the status evaluation index of each terminal device is combined with a preset evaluation judgment threshold set to perform status judgment; Among them, the evaluation threshold set is [60, 80]; The status is determined as follows: If Zg<60, it is determined that the g-th terminal device is in a bad state; If 60≤Zg<80, it is determined that the g-th terminal device is in a general state; If Zg≥80, the g-th terminal device is determined to be in good condition.

6. A smart distribution station multi-terminal equipment management system according to claim 1, characterized in that: The data collection unit is also used to collect maintenance personnel information used for operation and maintenance of each terminal equipment in the power distribution station; Maintenance personnel information includes: The qualification level coefficient, working years coefficient and working experience coefficient of the relevant maintenance personnel, wherein the qualification level coefficient, working years coefficient and working experience coefficient are pre-set parameters for each maintenance personnel; The central processing unit is also used to calculate the skill level index of each maintenance personnel based on the maintenance personnel information; The central processing unit is also used to classify the skill level indicators of each maintenance personnel in combination with a preset skill determination threshold set, and determine the priority of each maintenance personnel.

7. A smart distribution station multi-terminal equipment management system according to claim 6, characterized in that: The skill level index is calculated as follows: The skill level index of each maintenance personnel is obtained through R = U1 × β1 + U2 × β2 + U3 × β3; In the formula, R is the skill level index of the maintenance personnel, U1 is the qualification level coefficient of the maintenance personnel, U2 is the working years coefficient of the maintenance personnel, and U3 is the working experience coefficient of the maintenance personnel.

8. A smart distribution station multi-terminal equipment management system according to claim 7, characterized in that: The priority classification method is as follows: Among them, the skill determination threshold set is [12, 18]; If R < 12, the relevant maintenance personnel will be designated as high-priority maintenance objects; If 12≤R<18, the relevant maintenance personnel will be designated as medium priority maintenance objects; If R≥18, the relevant maintenance personnel will be designated as low-priority maintenance objects.

9. A smart distribution station multi-terminal equipment management system according to claim 8, characterized in that: Also includes: The device management unit is used to maintain and manage the terminal devices according to the analysis results of the central processing unit. The specific methods are as follows: For the state evaluation index Z g For terminal equipment with a bad status of less than 60%, maintenance personnel corresponding to high-priority maintenance objects will be arranged to inspect and repair them; For the state evaluation index 60≤Z g For general status terminal equipment with a value less than 80, maintenance personnel corresponding to the medium-priority maintenance object shall be arranged to inspect and repair it; For the state evaluation index Z g For terminal equipment in good condition with a value of ≥80, maintenance personnel corresponding to low-priority maintenance objects will be arranged to inspect and repair them.

10. A method for managing multi-terminal equipment in a smart distribution station, characterized in that: The method is implemented through a smart distribution station multi-terminal equipment management system as described in any one of claims 1-8.

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