An intelligent management system and method for electrical parameter data based on big data

Abstract

The present invention relates to the technical field of electric parameter data management, and specifically to an intelligent electric parameter data management system and method based on big data, comprising constructing an electric parameter management cloud platform, calculating the equipment operating efficiency deviation value of electrical equipment, and obtaining abnormal time data; analyzing the electric parameter reference range of the electric parameters in the electric parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtaining abnormal electric parameter reference data; analyzing the influence of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment, and optimizing and adjusting the electric parameter reference data of the electric cabinet equipment, and obtaining characteristic electric parameter reference data; monitoring the operating environment of the electric cabinet equipment in the current cycle, obtaining the electric parameters of the electric cabinet equipment in the current cycle, and evaluating the equipment status of the electric cabinet equipment in combination with the characteristic electric parameter reference data of the electric cabinet equipment, and performing intelligent management on the electric cabinet equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical parameter data management, and in particular to an intelligent management system and method for electrical parameter data based on big data. Background Art

[0002] Big data technology refers to the technology and methods used to collect, store, process and analyze large amounts of diverse and rapidly changing data. The application of big data technology brings a series of benefits to power parameter management, including but not limited to the following: 1. Enhanced prediction capabilities, using historical power parameter data and machine learning algorithms to predict equipment load, thereby helping power agencies to do a good job in power dispatch and resource allocation, and reduce the risk of power shortage or surplus; 2. Real-time early warning of equipment, quickly discovering equipment anomalies, and issuing early warnings in time to reduce potential safety risks; 3. Improve energy management capabilities. Through in-depth analysis of power parameter data, power agencies can achieve more intelligent energy management and improve the flexibility of the power system.

[0003] At present, in the analysis of the equipment status and faults of electrical cabinet equipment, the main method is to compare the corresponding values ​​of the electrical parameters of the electrical cabinet equipment obtained with the preset reference values ​​of the electrical parameters of the electrical cabinet equipment, so as to determine the equipment operating status of the electrical cabinet equipment and manage the equipment of the electrical cabinet equipment. However, in real life, the equipment models of various electrical cabinet equipment are different, and even the same electrical cabinet equipment has different environments and equipment for power transmission and distribution. These will affect the operating status of the electrical cabinet equipment. However, the preset reference values ​​of the electrical parameters of the electrical cabinet equipment are very simple and cannot be dynamically adjusted according to the equipment model of the electrical cabinet equipment and the working environment of the electrical cabinet equipment. Even if the electrical parameters of the electrical cabinet equipment are obtained, the preset reference values ​​of the electrical parameters do not conform to the actual situation of the electrical cabinet equipment, which can easily lead to errors in the judgment of the equipment status of the electrical cabinet equipment, not only easily causing damage to the electrical cabinet equipment, but also leading to the occurrence of safety accidents, resulting in huge losses. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent management system and method for electrical parameter data based on big data to solve the problems existing in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent management of electrical parameter data based on big data, the method comprising:

[0006] Step S100: Build an electrical parameter management cloud platform, use electrical cabinet equipment to distribute power to electrical equipment, obtain historical equipment operation data of the electrical equipment, calculate the equipment operation efficiency deviation value of the electrical equipment, and obtain abnormal time data;

[0007] Step S200: Obtain historical equipment operation records of the electrical cabinet equipment, and in combination with abnormal time data of the electrical cabinet equipment, obtain electrical parameter reference data of the electrical cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the degree of equipment matching of the electrical cabinet equipment, and obtain abnormal electrical parameter reference data;

[0008] Step S300: Obtain historical environmental change records of the electrical cabinet equipment, and analyze the degree of influence of the environmental parameters of the electrical cabinet equipment on different electrical parameters of the electrical cabinet equipment in combination with abnormal electrical parameter reference data, and optimize and adjust the electrical parameter reference data of the electrical cabinet equipment to obtain characteristic electrical parameter reference data;

[0009] Step S400: Monitor the operating environment of the electrical cabinet equipment in the current cycle, obtain the electrical parameters of the electrical cabinet equipment in the current cycle, evaluate the equipment status of the electrical cabinet equipment in combination with the characteristic electrical parameter reference data of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

[0010] Furthermore, step S100 includes:

[0011] Step S101: Build an electrical parameter management cloud platform to obtain historical equipment operation data of each electrical equipment responsible for power distribution in the electrical cabinet. The historical equipment operation data includes data corresponding to the input power and output power of the electrical equipment.

[0012] Step S102: Calculate the equipment operating efficiency η=P of the electrical equipment output / P input , where P output Expressed as the output power of electrical equipment, P input Expressed as the input power of the electrical equipment, the average value ηδ of the equipment operating efficiency of the electrical equipment is obtained;

[0013] Step S103: Calculate the equipment operating efficiency deviation value η of the electrical equipment △ =|η-η δ |, obtaining equipment operating efficiency deviation values ​​of various electrical devices used for power distribution by the electrical cabinet device; when the equipment operating efficiency deviation value of a certain electrical device among the various electrical devices is greater than a preset equipment operating efficiency deviation threshold value for the certain electrical device, obtaining a historical period during which the equipment operating efficiency deviation value of the certain electrical device was greater than the equipment operating efficiency deviation threshold value; determining that the electrical cabinet device distributed power to the certain electrical device abnormally during the historical period, and recording the historical period as an abnormal historical period for the electrical cabinet device;

[0014] Step S104: Acquire and aggregate several abnormal historical periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment.

[0015] Furthermore, step S200 includes:

[0016] Step S201: Acquire abnormal time data of the electrical cabinet equipment, obtain several abnormal historical time periods of the electrical cabinet equipment from the abnormal time data, obtain and mark historical equipment operation records of the electrical cabinet equipment in the several abnormal historical time periods, and obtain several marked historical equipment operation records of the electrical cabinet equipment;

[0017] Step S202: Obtain historical electrical parameter data of the electrical cabinet device from the marked historical device operation records, where the historical electrical parameter data includes data corresponding to various electrical parameters of the electrical cabinet device;

[0018] Step S203: Obtaining preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform. The electrical parameter reference data includes electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment, and obtaining average values ​​of various electrical parameters of the electrical cabinet equipment in several marked historical equipment operation records;

[0019] Step S204: Randomly select the bth electrical parameter from the electrical parameters of the electrical cabinet equipment. When the bth electrical parameter in the electrical cabinet equipment is the average value in a marked historical equipment operation record and is outside the electrical parameter reference range of the bth electrical parameter, the historical equipment operation record is recorded as an abnormal historical equipment operation record of the bth electrical parameter. Calculate the equipment matching value P between the bth electrical parameter in the electrical parameter reference data and the electrical cabinet equipment. b =M b / M sum , where M b It is expressed as the total number of abnormal historical equipment operation records of item b of the electrical parameter, M sum Indicates the total number of historical equipment operation records marked for electrical cabinet equipment;

[0020] Step S205: When the equipment matching value of the b-th electrical parameter and the electrical cabinet equipment is greater than the preset equipment matching threshold, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data matches the electrical cabinet equipment; otherwise, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data does not match the electrical cabinet equipment, and the b-th electrical parameter is recorded as a reference abnormal electrical parameter. Several reference abnormal electrical parameters in the electrical cabinet equipment are obtained and aggregated to obtain the abnormal electrical parameter reference data of the electrical cabinet equipment.

[0021] Furthermore, step S300 includes:

[0022] Step S301: monitoring and recording the environment of the electrical cabinet equipment during a historical period, obtaining a historical environmental change record of the electrical cabinet equipment, and obtaining data corresponding to various environmental parameters of the electrical cabinet equipment from the historical environmental change record;

[0023] Step S302: Obtaining historical device operation records of each electrical cabinet device, wherein the total number of historical device operation records and historical environmental change records of the electrical cabinet device are the same and the interval length is the same, obtaining data corresponding to various electrical parameters of the electrical cabinet device from the historical device operation records, and obtaining several reference abnormal electrical parameters of the electrical cabinet device from the abnormal electrical parameter reference data of the electrical cabinet device;

[0024] Step S303: The specific process of analyzing the influence of various environmental parameters of the electrical cabinet equipment on the e-th reference abnormal electrical parameter of the electrical cabinet equipment is to obtain the average value of the c-th environmental parameter of the electrical cabinet equipment in each historical environmental change record, and calculate the parameter influence coefficient r of the c-th environmental parameter on the e-th reference abnormal electrical parameter. c,e :

[0025]

[0026] Where, j represents the total number of historical equipment operation records of the electrical cabinet equipment; X e,i It is expressed as the average value of the e-th reference abnormal electrical parameter in the i-th historical equipment operation record; X e It is expressed as the mean value of the average value of the e-th reference abnormal electrical parameter in each historical equipment operation record; Y c,i Expressed as the average value of the cth environmental parameter in the i-th historical environmental change record; Y c It is expressed as the mean of the average values ​​of the cth environmental parameter in each historical environmental change record of the electrical cabinet equipment;

[0027] Step S304: Obtain the maximum value r of the coefficient of influence of various environmental parameters of the electrical cabinet equipment on the e-th reference abnormal electrical parameter e,max ,When the maximum value is greater than the parameter influence coefficient threshold, the environmental parameter corresponding to the maximum value is recorded as the target influencing environmental parameter of the e-th reference abnormal electrical parameter;

[0028] Step S305: Obtain the average value of several historical equipment operation records of the electrical cabinet equipment that are not marked, and calculate the target dynamic upper limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment. e,max :

[0029]

[0030] Where n represents the total number of historical equipment operation records that are not marked for electrical cabinet equipment; X z e represents the average value of the e-th reference abnormal electrical parameter in the z-th historical equipment operation record that is not marked; X z c represents the average value of the unmarked zth historical equipment operation record; μz Represents the average of the average values ​​of several unmarked historical equipment operation records; F represents the monitoring value of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in the electrical cabinet equipment; Fmax and Fmin represent the maximum and minimum values ​​of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in each historical environmental change record of the electrical cabinet equipment, respectively; k is a preset reference constant;

[0031] Step S306: Calculate the target dynamic lower limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment e,min :

[0032]

[0033] Get the target electrical parameter reference range S(e)∈[S e,min ,S e,max ], obtaining target electrical parameter reference ranges of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimizing the preset electrical parameter reference data of the electrical cabinet equipment to obtain characteristic electrical parameter reference data of the electrical cabinet equipment;

[0034] The above steps first analyze the degree of influence of various environmental parameters of the electrical cabinet equipment on several reference abnormal electrical parameters of the electrical cabinet equipment. Different environmental parameters of the electrical cabinet equipment have different degrees of influence on the reference abnormal electrical parameters of the electrical cabinet equipment. By calculating the parameter influence coefficients of various environmental parameters on several reference abnormal electrical parameters, the reasons for the abnormality of the preset electrical parameter reference data can be accurately analyzed, which also makes the adjustment of the electrical parameter reference data of the electrical cabinet equipment in subsequent steps more scientific and reasonable.

[0035] Furthermore, step S400 includes:

[0036] Step S401: Monitor the environment inside the electrical cabinet equipment, obtain data corresponding to various environmental parameters of the electrical cabinet equipment in the current cycle, obtain characteristic electrical parameter reference data of the electrical cabinet equipment, and obtain optimized electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment based on the data corresponding to various environmental parameters of the electrical cabinet equipment in the current cycle;

[0037] Step S402: Obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

[0038] In order to better implement the above method, an intelligent management system for electrical parameter data based on big data is also proposed. The system includes an abnormal time data module, an abnormal electrical parameter reference data module, a characteristic electrical parameter reference data module, and an intelligent management module.

[0039] Abnormal time data module, used to calculate the equipment operation efficiency deviation value of electrical equipment and obtain abnormal time data;

[0040] The abnormal electrical parameter reference data module is used to obtain the electrical parameter reference data of the electrical cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the equipment matching degree of the electrical cabinet equipment, and obtain the abnormal electrical parameter reference data;

[0041] The characteristic electric parameter reference data module is used to obtain the historical environmental change records of the electric cabinet equipment, and combine it with the abnormal electric parameter reference data to analyze the influence of the environmental parameters of the electric cabinet equipment on the different electric parameters of the electric cabinet equipment, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data;

[0042] The intelligent management module is used to monitor the operating environment of the electrical cabinet equipment, obtain the electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the equipment status of the electrical cabinet equipment in combination with the characteristic electrical parameter reference data of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

[0043] Furthermore, the abnormal time data module includes an equipment operation efficiency deviation value unit and an abnormal time data unit;

[0044] The equipment operation efficiency deviation value unit is used to obtain the historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment and calculate the equipment operation efficiency deviation value of the electrical equipment;

[0045] The abnormal time data unit is used to obtain and aggregate several abnormal historical periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment.

[0046] Furthermore, the abnormal electrical parameter reference data module includes a device matching value unit and an abnormal electrical parameter reference data unit;

[0047] The device matching value unit is used to obtain the preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform and calculate the device matching value between each electrical parameter in the electrical parameter reference data and the electrical cabinet equipment;

[0048] The abnormal electrical parameter reference data unit is used to obtain and collect several reference abnormal electrical parameters in the electrical cabinet equipment to obtain abnormal electrical parameter reference data of the electrical cabinet equipment.

[0049] Furthermore, the characteristic electric parameter reference data module includes a parameter influence coefficient unit and a characteristic electric parameter reference data unit;

[0050] The parameter influence coefficient unit is used to analyze the influence of various environmental parameters of the electrical cabinet equipment on several reference abnormal electrical parameters of the electrical cabinet equipment, and calculate the parameter influence coefficients of various environmental parameters on several reference abnormal electrical parameters;

[0051] The characteristic electrical parameter reference data unit is used to obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment.

[0052] Furthermore, the intelligent management module includes an intelligent management unit;

[0053] The intelligent management unit is used to obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and combine the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment to evaluate the operating status of the electrical cabinet equipment in the current cycle and perform intelligent management of the electrical cabinet equipment.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention realizes intelligent analysis of the equipment status of the electrical cabinet equipment. In the process of equipment analysis of the electrical cabinet equipment, the differences between different electrical cabinet equipment and the influence of the equipment environment on the electrical parameter reference range of the electrical cabinet are taken into consideration. In the analysis process, the preset electrical parameter reference data in the cloud platform is first considered to see whether it is consistent with the actual equipment situation of the electrical cabinet equipment, and the electrical parameters with abnormal references are found. When analyzing the influence of various environmental parameters in the electrical cabinet equipment on the abnormal reference electrical parameters, and considering that the corresponding values ​​of the environmental parameters in the electrical cabinet equipment are dynamically changing, the electrical parameter reference data of the electrical parameters of the electrical cabinet equipment are optimized and adjusted, so that the equipment status judgment of the electrical cabinet equipment through electrical parameters is more scientific and accurate, and the risk of failure of the electrical cabinet equipment is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 It is a module diagram of an intelligent management system and method for electrical parameter data based on big data of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0058] See also Figure 1 The present invention provides a technical solution: a method for intelligent management of electrical parameter data based on big data, the method comprising:

[0059] Step S100: Build an electrical parameter management cloud platform, use electrical cabinet equipment to distribute power to electrical equipment, obtain historical equipment operation data of the electrical equipment, calculate the equipment operation efficiency deviation value of the electrical equipment, and obtain abnormal time data;

[0060] Wherein, step S100 includes:

[0061] Step S101: Build an electrical parameter management cloud platform to obtain historical equipment operation data of each electrical equipment responsible for power distribution in the electrical cabinet. The historical equipment operation data includes data corresponding to the input power and output power of the electrical equipment.

[0062] Step S102: Calculate the equipment operating efficiency η=P of the electrical equipment output / P input , where P output Expressed as the output power of electrical equipment, P input Expressed as the input power of the electrical equipment, the average value ηδ of the equipment operating efficiency of the electrical equipment is obtained;

[0063] Step S103: Calculate the equipment operating efficiency deviation value η of the electrical equipment △ =|η-η δ |, obtaining equipment operating efficiency deviation values ​​of various electrical devices used for power distribution by the electrical cabinet device; when the equipment operating efficiency deviation value of a certain electrical device among the various electrical devices is greater than a preset equipment operating efficiency deviation threshold value for the certain electrical device, obtaining a historical period during which the equipment operating efficiency deviation value of the certain electrical device was greater than the equipment operating efficiency deviation threshold value; determining that the electrical cabinet device distributed power to the certain electrical device abnormally during the historical period, and recording the historical period as an abnormal historical period for the electrical cabinet device;

[0064] Step S104: Acquire and aggregate several abnormal historical periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment;

[0065] Step S200: Obtain historical equipment operation records of the electrical cabinet equipment, and in combination with abnormal time data of the electrical cabinet equipment, obtain electrical parameter reference data of the electrical cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the degree of equipment matching of the electrical cabinet equipment, and obtain abnormal electrical parameter reference data;

[0066] Wherein, step S200 includes:

[0067] Step S201: Acquire abnormal time data of the electrical cabinet equipment, obtain several abnormal historical time periods of the electrical cabinet equipment from the abnormal time data, obtain and mark historical equipment operation records of the electrical cabinet equipment in the several abnormal historical time periods, and obtain several marked historical equipment operation records of the electrical cabinet equipment;

[0068] Step S202: Obtain historical electrical parameter data of the electrical cabinet device from the marked historical device operation records, where the historical electrical parameter data includes data corresponding to various electrical parameters of the electrical cabinet device;

[0069] For example, the electrical parameters of the electrical cabinet equipment include the voltage, current, frequency, etc. of the electrical cabinet equipment;

[0070] Step S203: Obtaining preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform. The electrical parameter reference data includes electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment, and obtaining average values ​​of various electrical parameters of the electrical cabinet equipment in several marked historical equipment operation records;

[0071] Step S204: Randomly select the bth electrical parameter from the electrical parameters of the electrical cabinet equipment. When the bth electrical parameter in the electrical cabinet equipment is the average value in a marked historical equipment operation record and is outside the electrical parameter reference range of the bth electrical parameter, the historical equipment operation record is recorded as an abnormal historical equipment operation record of the bth electrical parameter. Calculate the equipment matching value P between the bth electrical parameter in the electrical parameter reference data and the electrical cabinet equipment. b =M b / M sum , where M b It is expressed as the total number of abnormal historical equipment operation records of item b of the electrical parameter, M sum Indicates the total number of historical equipment operation records marked for electrical cabinet equipment;

[0072] Step S205: When the device matching value of the b-th electrical parameter and the electrical cabinet device is greater than the preset device matching threshold, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data matches the electrical cabinet device; otherwise, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data does not match the electrical cabinet device, and the b-th electrical parameter is recorded as a reference abnormal electrical parameter. Several reference abnormal electrical parameters in the electrical cabinet device are obtained and aggregated to obtain abnormal electrical parameter reference data of the electrical cabinet device.

[0073] Step S300: Obtain historical environmental change records of the electrical cabinet equipment, and analyze the degree of influence of the environmental parameters of the electrical cabinet equipment on different electrical parameters of the electrical cabinet equipment in combination with abnormal electrical parameter reference data, and optimize and adjust the electrical parameter reference data of the electrical cabinet equipment to obtain characteristic electrical parameter reference data;

[0074] Wherein, step S300 includes:

[0075] Step S301: monitoring and recording the environment of the electrical cabinet equipment during a historical period, obtaining a historical environmental change record of the electrical cabinet equipment, and obtaining data corresponding to various environmental parameters of the electrical cabinet equipment from the historical environmental change record;

[0076] For example, various environmental parameters include temperature and humidity inside the electrical cabinet equipment;

[0077] Step S302: Obtaining historical device operation records of each electrical cabinet device, wherein the total number of historical device operation records and historical environmental change records of the electrical cabinet device are the same and the interval length is the same, obtaining data corresponding to various electrical parameters of the electrical cabinet device from the historical device operation records, and obtaining several reference abnormal electrical parameters of the electrical cabinet device from the abnormal electrical parameter reference data of the electrical cabinet device;

[0078] Step S303: The specific process of analyzing the influence of various environmental parameters of the electrical cabinet equipment on the e-th reference abnormal electrical parameter of the electrical cabinet equipment is to obtain the average value of the c-th environmental parameter of the electrical cabinet equipment in each historical environmental change record, and calculate the parameter influence coefficient r of the c-th environmental parameter on the e-th reference abnormal electrical parameter. c,e :

[0079]

[0080] Where, j represents the total number of historical equipment operation records of the electrical cabinet equipment; X e,i It is expressed as the average value of the e-th reference abnormal electrical parameter in the i-th historical equipment operation record; X e It is expressed as the mean value of the average value of the e-th reference abnormal electrical parameter in each historical equipment operation record; Y c,iExpressed as the average value of the cth environmental parameter in the i-th historical environmental change record; Y c It is expressed as the mean of the average values ​​of the cth environmental parameter in each historical environmental change record of the electrical cabinet equipment;

[0081] For example, the total number j of each historical equipment operation record of the electrical cabinet equipment is 3; the average value of the abnormal electrical parameter X of the second item in the first historical equipment operation record is 2,1 Expressed as 5; the average value of the second reference abnormal electrical parameter in the second historical equipment operation record is X 2,2 Expressed as 2; the second item in the third historical equipment operation record refers to the average value of the abnormal electrical parameter X 2,3 The average value of the second item of the reference abnormal electrical parameter in each historical equipment operation record is X2, which is expressed as 4; the average value of the third item of the environmental parameter in the first historical environmental change record is Y 3,1 Expressed as 10; the average value Y of the third environmental parameter in the second historical environmental change record 3,2 Expressed as 6; the average value Y of the third environmental parameter in the third historical environmental change record 3,3 It is expressed as 5; the mean value Y3 of the average value of the third environmental parameter in each historical environmental change record of the electrical cabinet equipment is expressed as 7;

[0082] Calculate the parameter influence coefficient r of the third environmental parameter on the second reference abnormal electrical parameter 3,2 :

[0083]

[0084] Step S304: Obtain the maximum value r of the coefficient of influence of various environmental parameters of the electrical cabinet equipment on the e-th reference abnormal electrical parameter e,max ,When the maximum value is greater than the parameter influence coefficient threshold, the environmental parameter corresponding to the maximum value is recorded as the target influencing environmental parameter of the e-th reference abnormal electrical parameter;

[0085] Step S305: Obtain the average value of several historical equipment operation records of the electrical cabinet equipment that are not marked, and calculate the target dynamic upper limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment. e,max :

[0086]

[0087] Where n represents the total number of historical equipment operation records that are not marked for electrical cabinet equipment; X z e represents the average value of the e-th reference abnormal electrical parameter in the z-th historical equipment operation record that is not marked; X zc represents the average value of the unmarked zth historical equipment operation record; μ z Represents the average of the average values ​​of several unmarked historical equipment operation records; F represents the monitoring value of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in the electrical cabinet equipment; Fmax and Fmin represent the maximum and minimum values ​​of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in each historical environmental change record of the electrical cabinet equipment, respectively; k is a preset reference constant;

[0088] Step S306: Calculate the target dynamic lower limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment e,min :

[0089]

[0090] Get the target electrical parameter reference range S(e)∈[S e,min ,S e,max ], obtaining target electrical parameter reference ranges of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimizing the preset electrical parameter reference data of the electrical cabinet equipment to obtain characteristic electrical parameter reference data of the electrical cabinet equipment;

[0091] Step S400: Monitor the operating environment of the electrical cabinet equipment in the current cycle, obtain the electrical parameters of the electrical cabinet equipment in the current cycle, evaluate the equipment status of the electrical cabinet equipment in combination with the characteristic electrical parameter reference data of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment;

[0092] Wherein, step S400 includes:

[0093] Step S401: Monitor the environment inside the electrical cabinet equipment, obtain data corresponding to various environmental parameters of the electrical cabinet equipment in the current cycle, obtain characteristic electrical parameter reference data of the electrical cabinet equipment, and obtain optimized electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment based on the data corresponding to various environmental parameters of the electrical cabinet equipment in the current cycle;

[0094] Step S402: Acquire data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference ranges of the various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment;

[0095] In order to better implement the above method, an intelligent management system for electrical parameter data based on big data is also proposed. The system includes an abnormal time data module, an abnormal electrical parameter reference data module, a characteristic electrical parameter reference data module, and an intelligent management module.

[0096] Abnormal time data module, used to calculate the equipment operation efficiency deviation value of electrical equipment and obtain abnormal time data;

[0097] The abnormal electrical parameter reference data module is used to obtain the electrical parameter reference data of the electrical cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the equipment matching degree of the electrical cabinet equipment, and obtain the abnormal electrical parameter reference data;

[0098] The characteristic electric parameter reference data module is used to obtain the historical environmental change records of the electric cabinet equipment, and combine it with the abnormal electric parameter reference data to analyze the influence of the environmental parameters of the electric cabinet equipment on the different electric parameters of the electric cabinet equipment, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data;

[0099] The intelligent management module is used to monitor the operating environment of the electrical cabinet equipment, obtain the electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the equipment status of the electrical cabinet equipment in combination with the characteristic electrical parameter reference data of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment;

[0100] Among them, the abnormal time data module includes an equipment operation efficiency deviation value unit and an abnormal time data unit;

[0101] The equipment operation efficiency deviation value unit is used to obtain the historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment and calculate the equipment operation efficiency deviation value of the electrical equipment;

[0102] The abnormal time data unit is used to obtain and aggregate several abnormal historical periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment;

[0103] Among them, the abnormal electrical parameter reference data module includes a device matching value unit and an abnormal electrical parameter reference data unit;

[0104] The device matching value unit is used to obtain the preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform and calculate the device matching value between each electrical parameter in the electrical parameter reference data and the electrical cabinet equipment;

[0105] The abnormal electrical parameter reference data unit is used to obtain and collect several reference abnormal electrical parameters in the electrical cabinet equipment to obtain abnormal electrical parameter reference data of the electrical cabinet equipment;

[0106] Among them, the characteristic electric parameter reference data module includes a parameter influence coefficient unit and a characteristic electric parameter reference data unit;

[0107] The parameter influence coefficient unit is used to analyze the influence of various environmental parameters of the electrical cabinet equipment on several reference abnormal electrical parameters of the electrical cabinet equipment, and calculate the parameter influence coefficients of various environmental parameters on several reference abnormal electrical parameters;

[0108] The characteristic electric parameter reference data unit is used to obtain the target electric parameter reference range of several reference abnormal electric parameters of the electric cabinet equipment, and optimize the preset electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data of the electric cabinet equipment;

[0109] Wherein, the intelligent management module includes an intelligent management unit;

[0110] The intelligent management unit is used to obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and combine the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment to evaluate the operating status of the electrical cabinet equipment in the current cycle and perform intelligent management of the electrical cabinet equipment.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0112] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for intelligent management of electrical parameter data based on big data, characterized in that: The method comprises: Step S100: constructing an electrical parameter management cloud platform, using electrical cabinet equipment to distribute power to electrical equipment, obtaining historical equipment operation data of the electrical equipment, calculating the equipment operation efficiency deviation value of the electrical equipment, and obtaining abnormal time data; Step S200: Obtain historical equipment operation records of the electrical cabinet equipment, and in combination with the abnormal time data of the electrical cabinet equipment, obtain electrical parameter reference data of the electrical cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the degree of equipment matching of the electrical cabinet equipment, and obtain abnormal electrical parameter reference data; Step S300: Obtaining historical environmental change records of the electrical cabinet equipment, and combining the abnormal electrical parameter reference data, analyzing the degree of influence of the environmental parameters of the electrical cabinet equipment on different electrical parameters of the electrical cabinet equipment, and optimizing and adjusting the electrical parameter reference data of the electrical cabinet equipment to obtain characteristic electrical parameter reference data; Step S400: Monitor the operating environment of the electrical cabinet equipment in the current cycle, obtain the electrical parameters of the electrical cabinet equipment in the current cycle, evaluate the equipment status of the electrical cabinet equipment in combination with the characteristic electrical parameter reference data of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

2. The method for intelligent management of electrical parameter data based on big data according to claim 1, characterized in that: The step S100 includes: Step S101: Build an electrical parameter management cloud platform to obtain historical equipment operation data of each electrical equipment responsible for power distribution in the electrical cabinet, wherein the historical equipment operation data includes data corresponding to the input power and output power of the electrical equipment; Step S102: Calculate the equipment operating efficiency η=P of the electrical equipment output / P input , where P output Expressed as the output power of the electrical equipment, P input Expressed as the input power of the electrical equipment, the average value η of the equipment operating efficiency of the electrical equipment is obtained. δ ; Step S103: Calculate the equipment operating efficiency deviation value η of the electrical equipment △ =|η-η δ |, obtaining equipment operating efficiency deviation values ​​of various electrical devices used for power distribution by the electrical cabinet device; when the equipment operating efficiency deviation value of a certain electrical device among the various electrical devices is greater than a preset equipment operating efficiency deviation threshold value for the certain electrical device, obtaining a historical period during which the equipment operating efficiency deviation value of the certain electrical device was greater than the equipment operating efficiency deviation threshold value; determining that the electrical cabinet device distributed power to the certain electrical device abnormally during the historical period; and recording the historical period as an abnormal historical period for the electrical cabinet device; Step S104: Acquire and aggregate several abnormal historical time periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment.

3. The method for intelligent management of electrical parameter data based on big data according to claim 2, characterized in that: The step S200 includes: Step S201: Acquire abnormal time data of the electrical cabinet device, obtain several abnormal historical time periods of the electrical cabinet device from the abnormal time data, obtain and mark historical equipment operation records of the electrical cabinet device within the several abnormal historical time periods, and obtain several marked historical equipment operation records of the electrical cabinet device; Step S202: Acquire historical electrical parameter data of the electrical cabinet device from the marked historical device operation records, wherein the historical electrical parameter data includes data corresponding to various electrical parameters of the electrical cabinet device; Step S203: Obtaining preset electrical parameter reference data of the electrical cabinet device from the cloud platform, the electrical parameter reference data including electrical parameter reference ranges of various electrical parameters of the electrical cabinet device, and obtaining average values ​​of various electrical parameters of the electrical cabinet device in several marked historical device operation records; Step S204: Randomly select the bth electrical parameter from the electrical parameters of the electrical cabinet device. When the bth electrical parameter in the electrical cabinet device is an average value in a marked historical device operation record and is outside the electrical parameter reference range of the bth electrical parameter, the historical device operation record is recorded as an abnormal historical device operation record of the bth electrical parameter. Calculate the device matching value P between the bth electrical parameter in the electrical parameter reference data and the electrical cabinet device. b =M b / M sum , where M b It is represented by the total number of abnormal historical equipment operation records of the electrical parameter item b, M sum Indicates the total number of historical device operation records marked for the electrical cabinet device; Step S205: When the device matching value of the b-th electrical parameter and the electrical cabinet equipment is greater than the preset device matching threshold, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data matches the electrical cabinet equipment; otherwise, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data does not match the electrical cabinet equipment, and the b-th electrical parameter is recorded as a reference abnormal electrical parameter. Several reference abnormal electrical parameters in the electrical cabinet equipment are obtained and aggregated to obtain the abnormal electrical parameter reference data of the electrical cabinet equipment.

4. The method for intelligent management of electrical parameter data based on big data according to claim 3, characterized in that: The step S300 includes: Step S301: monitoring and recording the environment of the electrical cabinet device within a historical period, obtaining a historical environmental change record of the electrical cabinet device, and acquiring data corresponding to various environmental parameters of the electrical cabinet device from the historical environmental change record; Step S302: Obtaining historical device operation records of the electrical cabinet device, wherein the total number of historical device operation records and historical environmental change records of the electrical cabinet device are the same and the interval length is the same, obtaining data corresponding to various electrical parameters of the electrical cabinet device from the historical device operation records, and obtaining several reference abnormal electrical parameters of the electrical cabinet device from the abnormal electrical parameter reference data of the electrical cabinet device; Step S303: The specific process of analyzing the degree of influence of various environmental parameters of the electrical cabinet equipment on the e-th reference abnormal electrical parameter of the electrical cabinet equipment is to obtain the average value of the c-th environmental parameter of the electrical cabinet equipment in each historical environmental change record, and calculate the parameter influence coefficient r of the c-th environmental parameter on the e-th reference abnormal electrical parameter. c,e : Wherein, j represents the total number of historical equipment operation records of the electrical cabinet equipment; X e,i It is represented by the average value of the e-th reference abnormal electrical parameter in the i-th historical equipment operation record; X e Y is represented by the mean of the average values ​​of the e-th reference abnormal electrical parameters in the historical equipment operation records; c,i Y is expressed as the average value of the cth environmental parameter in the i-th historical environmental change record; c It is represented by the mean of the average values ​​of the cth environmental parameter in each historical environmental change record of the electrical cabinet equipment; Step S304: Obtain the maximum value r of the coefficient of influence of various environmental parameters of the electrical cabinet equipment on the e-th reference abnormal electrical parameter e,max When the maximum value is greater than the parameter influence coefficient threshold, the environmental parameter corresponding to the maximum value is recorded as the target influencing environmental parameter of the e-th reference abnormal electrical parameter; Step S305: Obtain the average value of several historical equipment operation records of the electrical cabinet equipment that are not marked, and calculate the target dynamic upper limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment. e,max : Where n represents the total number of unmarked historical equipment operation records of the electrical cabinet equipment; X z e represents the average value of the e-th reference abnormal electrical parameter in the z-th historical equipment operation record that is not marked; X z c represents the average value of the unmarked zth historical equipment operation record; μ z represents the mean of the average values ​​of several unmarked historical equipment operation records; F represents the monitoring value of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in the electrical cabinet equipment; Fmax and Fmin represent the maximum and minimum values ​​of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in each historical environmental change record of the electrical cabinet equipment, respectively; k is a preset reference constant; Step S306: Calculate the target dynamic lower limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment e,min : Get the target electrical parameter reference range S(e)∈[S e,min ,S e,max ], obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment.

5. The method for intelligent management of electrical parameter data based on big data according to claim 4, characterized in that: The step S400 includes: Step S401: monitoring the environment within the electrical cabinet equipment, obtaining data corresponding to various environmental parameters of the electrical cabinet equipment in a current cycle, obtaining characteristic electrical parameter reference data of the electrical cabinet equipment, and obtaining optimized electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment based on the data corresponding to various environmental parameters of the electrical cabinet equipment in the current cycle; Step S402: Obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

6. An intelligent management system for electrical parameter data based on big data, used to execute an intelligent management method for electrical parameter data based on big data according to any one of claims 1 to 5, characterized in that: The system includes an abnormal time data module, an abnormal electrical parameter reference data module, a characteristic electrical parameter reference data module, and an intelligent management module; The abnormal time data module is used to calculate the equipment operation efficiency deviation value of the electrical equipment to obtain abnormal time data; The abnormal electrical parameter reference data module is used to obtain the electrical parameter reference data of the electrical cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the degree of equipment matching of the electrical cabinet equipment, and obtain the abnormal electrical parameter reference data; The characteristic electric parameter reference data module is used to obtain the historical environmental change records of the electric cabinet equipment, and combine the abnormal electric parameter reference data to analyze the degree of influence of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain characteristic electric parameter reference data; The intelligent management module is used to monitor the operating environment of the electrical cabinet equipment, obtain the electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the equipment status of the electrical cabinet equipment in combination with the characteristic electrical parameter reference data of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

7. The intelligent management system for electrical parameter data based on big data according to claim 6, characterized in that: The abnormal time data module includes an equipment operation efficiency deviation value unit and an abnormal time data unit; The equipment operation efficiency deviation value unit is used to obtain historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment, and calculate the equipment operation efficiency deviation value of the electrical equipment; The abnormal time data unit is used to acquire and collect several abnormal historical time periods of the electric cabinet equipment to obtain abnormal time data of the electric cabinet equipment.

8. The intelligent management system for electrical parameter data based on big data according to claim 6, characterized in that: The abnormal electrical parameter reference data module includes a device matching value unit and an abnormal electrical parameter reference data unit; The device matching value unit is used to obtain the preset electrical parameter reference data of the electrical cabinet device from the cloud platform, and calculate the device matching value between each electrical parameter in the electrical parameter reference data and the electrical cabinet device; The abnormal electrical parameter reference data unit is used to acquire and collect several reference abnormal electrical parameters in the electrical cabinet equipment to obtain abnormal electrical parameter reference data of the electrical cabinet equipment.

9. The intelligent management system for electrical parameter data based on big data according to claim 6, characterized in that: The characteristic electric parameter reference data module includes a parameter influence coefficient unit and a characteristic electric parameter reference data unit; The parameter influence coefficient unit is used to analyze the influence of various environmental parameters of the electric cabinet equipment on several reference abnormal electrical parameters of the electric cabinet equipment, and calculate the parameter influence coefficients of the various environmental parameters on the several reference abnormal electrical parameters; The characteristic electrical parameter reference data unit is used to obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment.

10. The intelligent management system for electrical parameter data based on big data according to claim 6, characterized in that: The intelligent management module includes an intelligent management unit; The intelligent management unit is used to obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and combine the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment to evaluate the operating status of the electrical cabinet equipment in the current cycle and perform intelligent management of the electrical cabinet equipment.

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