A smart host management system suitable for a medium voltage cabinet

Abstract

The application discloses a kind of intelligent host management systems suitable for medium voltage cabinet, it is related to medium voltage cabinet management technical field, including management system including equipment reference degree evaluation module, characteristic parameter effect data module, target effect data module, model optimization module, intelligent host management module;Equipment reference degree evaluation module is used to evaluate the equipment reference degree of historical medium voltage cabinet to medium voltage cabinet;Characteristic parameter effect data module is used to evaluate the effect degree between different equipment operating parameters in reference historical medium voltage cabinet;Target effect data module is used to analyze the effect between equipment operating parameters in medium voltage cabinet;Model optimization module is used to optimize fault detection model;Intelligent host management module is used to upload equipment evaluation data to the mobile communication device of user by intelligent host, and intelligent host carries out intelligent equipment management to medium voltage cabinet according to the instruction of user in mobile communication device.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medium voltage cabinet management, and particularly relates to a smart host management system suitable for a medium voltage cabinet. BACKGROUND

[0002] The medium voltage cabinet is a device in a power system, and is mainly used for power distribution and control of medium voltage power. The medium voltage cabinet usually converts high voltage power into medium voltage power, and plays a role in power distribution and protection in a medium voltage power grid. At present, the main method for device management of the medium voltage cabinet is to use sensors to monitor device parameters such as voltage, current and temperature of the medium voltage cabinet, and analyze the device state of the medium voltage cabinet according to upper and lower threshold values of the device parameters set for normal operation of the medium voltage cabinet. When the measured value corresponding to the real-time collected device parameters exceeds the preset range, an alarm is triggered. However, in actual situations, the device model, device use environment and mutual influence between different device parameters of the medium voltage cabinet all cause the actual normal operation range of the device parameters to change. Moreover, the user cannot currently use a mobile communication device to real-time grasp and control the device condition of the medium voltage cabinet. These situations not only cause incorrect determination of the device condition of the medium voltage cabinet, but also cause the user to be unable to real-time grasp the device condition of the medium voltage cabinet, which not only leads to device damage of the medium voltage cabinet, but also may even cause serious safety accidents and result in significant losses. SUMMARY

[0003] The present application aims to provide a smart host management system suitable for a medium voltage cabinet to solve the problems in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a smart host management system suitable for a medium voltage cabinet, the management system comprising a device reference degree evaluation module, a characteristic parameter action data module, a target action data module, a model optimization module and a smart host management module.

[0005] The device reference degree evaluation module is used to acquire device information and historical device use environment records in the medium voltage cabinet and historical medium voltage cabinets, extract historical device use environment data from the historical device use environment records, evaluate the device reference degree of the historical medium voltage cabinets to the medium voltage cabinet, and obtain reference historical medium voltage cabinets.

[0006] The characteristic parameter action data module is used to acquire historical operation records of the reference historical medium voltage cabinets of the medium voltage cabinet, acquire historical operation data from the historical operation records, evaluate the action degree between different device operation parameters in the reference historical medium voltage cabinets, and obtain characteristic parameter action data.

[0007] The target action data module is used to refer to the historical characteristic parameter action data of medium-voltage switchgear, calculate the characteristic action values ​​between the operating parameters of different equipment in the medium-voltage switchgear, analyze the interaction between the operating parameters of the equipment in the medium-voltage switchgear, and obtain the target action data of the medium-voltage switchgear.

[0008] The model optimization module is used to acquire target action data of the medium-voltage switchgear, acquire historical fault operation records of the medium-voltage switchgear, and optimize the preset fault detection model of the medium-voltage switchgear.

[0009] The intelligent host management module is used to monitor the operating status of medium-voltage switchgear using an intelligent host, generate equipment evaluation data, and upload the equipment evaluation data to the user's mobile communication device via the intelligent host. The intelligent host then performs intelligent equipment management of the medium-voltage switchgear based on the user's instructions on the mobile communication device.

[0010] Furthermore, the equipment reference level assessment module includes an equipment screening unit and an equipment reference level assessment unit;

[0011] The equipment screening unit is used to build a management cloud platform to acquire equipment information of medium-voltage switchgear. The equipment information includes the equipment model of the medium-voltage switchgear. The unit retrieves the equipment information of each historical medium-voltage switchgear from the cloud platform, filters the historical medium-voltage switchgear based on the equipment model of the medium-voltage switchgear, and retains the historical medium-voltage switchgear with the same equipment model as the medium-voltage switchgear, thus obtaining several historical medium-voltage switchgear.

[0012] The equipment reference degree assessment unit is used to acquire historical equipment usage environment records of medium-voltage switchgear and historical medium-voltage switchgear, calculate the reference degree of historical medium-voltage switchgear to the marked equipment of medium-voltage switchgear, and evaluate the equipment reference degree of historical medium-voltage switchgear to the medium-voltage switchgear based on the reference degree of marked equipment, so as to obtain the reference historical medium-voltage switchgear of the medium-voltage switchgear.

[0013] Furthermore, the equipment reference level assessment unit includes:

[0014] The historical equipment usage environment records of the medium-voltage switchgear are acquired, and the historical equipment usage environment data is extracted from the historical equipment usage environment records. The historical equipment usage environment data includes the data corresponding to various environmental parameters.

[0015] Obtain historical equipment usage environment records for several historical medium-voltage switchgear, and extract historical equipment usage environment data from these records;

[0016] Obtain the maximum and minimum values ​​of the average values ​​of various environmental parameters from the historical equipment usage environment records of the medium-voltage switchgear. Calculate the characteristic values ​​of each environmental parameter in the medium-voltage switchgear, where the characteristic value B of the a-th environmental parameter in the medium-voltage switchgear is... a :

[0017] ,

[0018] wherein, n represents the total number of each historical equipment use environment record of the medium voltage cabinet; C a,i represents the average value of the a-th environmental parameter in the i-th historical equipment use environment record; μ a represents the average value of the a-th environmental parameter in each historical equipment use record of the medium voltage cabinet; C a,max , C a,min respectively represent the maximum value C a,max , the minimum value C a,min of the average value of the a-th environmental parameter in each historical equipment use environment record of the medium voltage cabinet; C

[0019] The degree of reference of the medium voltage cabinet to the equipment of the medium voltage cabinet is evaluated, and the specific process of evaluating the degree of reference of the d-th historical medium voltage cabinet to the equipment of the medium voltage cabinet is as follows:

[0020] The characteristic value of each environmental parameter in the d-th historical medium voltage cabinet is obtained, and the marked equipment reference degree E d of the d-th historical medium voltage cabinet to the medium voltage cabinet is calculated.

[0021] ,

[0022] wherein, m represents the total number of environmental parameters of the medium voltage cabinet; B x represents the characteristic value of the x-th environmental parameter in the medium voltage cabinet; B d x represents the characteristic value of the x-th environmental parameter in the d-th historical medium voltage cabinet;

[0023] When the marked equipment reference degree E d is greater than the preset marked equipment reference degree threshold value, it is determined that the d-th historical medium voltage cabinet has equipment reference value to the medium voltage cabinet, and the d-th historical medium voltage cabinet is recorded as a reference historical medium voltage cabinet of the medium voltage cabinet.

[0024] Further, the characteristic parameter action data module comprises a characteristic parameter action data unit.

[0025] The characteristic parameter action data unit is used to obtain each reference historical medium voltage cabinet of the medium voltage cabinet, obtain the historical running record of the reference historical medium voltage cabinet, evaluate the action degree between each equipment running parameter in the reference historical medium voltage cabinet, and obtain the characteristic parameter action data of the reference historical medium voltage cabinet.

[0026] Further, the characteristic parameter action data unit comprises:

[0027] The historical reference medium-voltage switchgear is acquired, the historical operation records of the reference historical medium-voltage switchgear are obtained, and the historical operation data is obtained from the historical operation records. The historical operation data includes the data corresponding to the operation parameters of various equipment in the reference historical medium-voltage switchgear.

[0028] The process of assessing the interaction between various equipment operating parameters in the historical reference medium-voltage switchgear, specifically assessing the effect of equipment operating parameter f on equipment operating parameter g, is as follows:

[0029] Obtain the mean value of the f-th equipment operating parameter from each historical operation record of the reference historical medium-voltage switchgear, and construct a parameter interaction regression model y between the f-th equipment operating parameter and the g-th equipment operating parameter in the reference historical medium-voltage switchgear. g :

[0030] ,

[0031] Where U0 represents the intercept; U f This represents the regression coefficient of the f-th equipment operating parameter in the reference historical medium-voltage switchgear; ε is the error term.

[0032] Based on a preset partitioning ratio, the historical operation records of each medium-voltage switchgear in the reference history are divided into a training set and a test set. Using the training set, the parameter effect regression model y is calculated. g The characteristic regression error S g :

[0033] ,

[0034] Where β is the total number of historical equipment operation records of the reference history switchgear in the training set; y g,q This is to refer to the actual value of the average value of the g-th equipment operating parameter in the q-th historical equipment operation record of the historical medium-voltage switchgear in the training set; y 、 g,q The parameters act on the regression model y g The predicted value of the mean value of the g-th equipment operating parameter within the q-th historical equipment operation record;

[0035] For the characteristic regression error S g Minimize, obtain parameters for the regression model y g Design matrix X:

[0036] ,

[0037] Where, x f,1 x f,2 ... x f,βrespectively represent the mean value of the fth equipment operation parameter in the 1st, 2nd, …, βth historical equipment operation record of the reference historical medium voltage cabinet in the training set;

[0038] obtain a vector U of regression coefficients in the parameter effect regression model y g

[0039] ,

[0040] wherein, y g,1 , y g,2 , …, y g,β respectively represent the mean value of the gth equipment operation parameter in the 1st, 2nd, …, βth historical equipment operation record of the reference historical medium voltage cabinet in the training set;

[0041] obtain a vector U of regression coefficients in the parameter effect regression model y g

[0042] ,

[0043] wherein, U = (U0, U f );

[0044] using the test set, calculate the root mean square error R g of the parameter effect regression model y g :

[0045] ,

[0046] wherein, ζ represents the total number of historical equipment operation records of the reference historical medium voltage cabinet in the test set; y △ g,z is the actual value of the mean value of the gth equipment operation parameter in the zth historical equipment operation record of the reference historical medium voltage cabinet in the test set; y △、 g,z is the predicted value of the mean value of the gth equipment operation parameter in the zth historical equipment operation record by the parameter effect regression model y g

[0047] when the root mean square error R g is less than a preset error threshold, it is determined that the parameter effect regression model yg is constructed, and the regression coefficient U g of the fth equipment operation parameter in the parameter effect regression model y f is obtained and normalized;

[0048] obtain the regression coefficient U g in the parameter effect regression model y f , when the regression coefficient U f ​​​If the absolute value of the characteristic regression threshold U' is greater than a preset characteristic regression threshold, it is determined that the fth equipment operation parameter in the reference history of the medium voltage cabinet has an influence degree on the gth equipment operation parameter, and the fth equipment operation parameter is recorded as the gth equipment operation parameter.

[0049] The influence data of the characteristic parameters of the reference history medium voltage cabinet is obtained by collecting the influence data of the characteristic parameters of several equipment operation parameters in the reference history medium voltage cabinet.

[0050] Further, the target influence data module includes a characteristic influence value unit and a target influence data unit.

[0051] The characteristic influence value unit is configured to obtain the influence data of the characteristic parameters of each reference history medium voltage cabinet of the medium voltage cabinet, and calculate the characteristic influence values between the equipment operation parameters in the medium voltage cabinet.

[0052] The target influence data unit is configured to analyze the influence between the equipment operation parameters in the medium voltage cabinet according to the characteristic influence values, and obtain the target influence data of the medium voltage cabinet.

[0053] Further, the characteristic influence value unit includes:

[0054] The influence data of the characteristic parameters of each reference history medium voltage cabinet of the medium voltage cabinet is obtained, and the influence operation parameters corresponding to the equipment operation parameters in the reference history medium voltage cabinet are obtained from the influence data of the characteristic parameters.

[0055] The characteristic influence values between the equipment operation parameters in the medium voltage cabinet are calculated, wherein the characteristic influence value K h,v of the hth equipment operation parameter on the vth equipment operation parameter in the medium voltage cabinet is calculated as follows:

[0056] The hth equipment operation parameter in the influence data of the characteristic parameters is obtained, and the characteristic influence values K h,v of the hth equipment operation parameter on the vth equipment operation parameter in the influence operation parameters of several reference history medium voltage cabinets are calculated.

[0057] ,

[0058] Wherein, δ represents the total number of the reference history medium voltage cabinets of the medium voltage cabinet, and δ is greater than a preset characteristic number threshold; U h v,α represents the regression coefficient of the vth equipment operation parameter in the parameter influence regression model of the hth equipment operation parameter on the vth equipment operation parameter in the αth reference history medium voltage cabinet of the medium voltage cabinet.

[0059] Further, the target influence data unit includes:

[0060] When the characteristic action value K h,v When the characteristic action value K h,v is greater than the preset first characteristic action threshold K1, it is determined that the hth equipment operation parameter in the medium voltage cabinet has a positive effect on the vth equipment operation parameter, and the hth equipment operation parameter is recorded as a positive effect equipment operation parameter of the vth equipment operation parameter;

[0061] When the characteristic action value K h,v is less than the preset second characteristic action threshold K2, it is determined that the hth equipment operation parameter in the medium voltage cabinet has a negative effect on the vth equipment operation parameter, and the hth equipment operation parameter is recorded as a negative effect equipment operation parameter of the vth equipment operation parameter, wherein K1>0>K2;

[0062] The target action data of the medium voltage cabinet is obtained by collecting the positive effect equipment operation parameters and the negative effect equipment operation parameters of the medium voltage cabinet.

[0063] Further, the model optimization module comprises a model optimization unit;

[0064] The model optimization unit is configured to obtain the target action data of the medium voltage cabinet, obtain the historical fault operation record of the medium voltage cabinet, obtain the preset fault detection model of the medium voltage cabinet from the cloud platform, obtain the positive effect equipment operation parameters and the negative effect equipment operation parameters of the medium voltage cabinet, obtain the characteristic action values between the plurality of equipment operation parameters and the positive effect equipment operation parameters or the negative effect equipment operation parameters, input the characteristic action values into the preset fault detection model, and optimize the fault detection model in combination with the historical fault operation record of the medium voltage cabinet.

[0065] Further, the intelligent host management module comprises an intelligent host management unit;

[0066] The intelligent host management unit is configured to use the intelligent host to monitor the equipment operation state of the medium voltage cabinet in the current period, input the data monitored by the intelligent host into the fault detection model, generate equipment evaluation data of the medium voltage cabinet by the fault detection model, and upload the equipment evaluation data to the mobile communication device of the user through the intelligent host, wherein the equipment evaluation data comprises the equipment fault probability of the medium voltage cabinet and the corresponding data of each equipment operation parameter, and the intelligent host performs intelligent equipment management on the medium voltage cabinet according to the instructions of the user on the mobile communication device.

[0067] In the above steps, the intelligent host can not only monitor the equipment status of the medium-voltage cabinet, but also connect to the user's mobile communication device. The user can view the equipment status of the medium-voltage cabinet in real time through the mobile communication device. The user can take a series of measures on the medium-voltage cabinet through the mobile communication device, which not only makes it easier for the user to grasp the equipment information of the medium-voltage cabinet, but also greatly reduces the risk of medium-voltage cabinet failure.

[0068] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention achieves intelligent management of medium-voltage switchgear. Considering that in actual situations, the equipment model, operating environment, and mutual influence between different equipment parameters in the switchgear can all cause changes in the actual normal operating range of the equipment parameters, the invention first obtains historical medium-voltage switchgear that provides equipment references, and analyzes the interaction of equipment operating parameters in these historical switchgear to determine the role of equipment operating parameters within the switchgear. This also optimizes the fault detection model. Simultaneously, a smart host monitors the operating status of the medium-voltage switchgear and uploads real-time detection data to the user's mobile communication device. This not only allows the user to promptly grasp the equipment status of the switchgear but also makes the results of the smart host's detection of the switchgear more accurate, greatly reducing the probability of equipment failure and lowering the possibility of safety accidents. Attached Figure Description

[0069] Fig. 1 This is a unit schematic diagram of an intelligent host management system applicable to medium-voltage switchgear according to the present invention;

[0070] Fig. 2 This is a schematic diagram of a module of an intelligent host management system applicable to medium-voltage switchgear according to the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] Example: Figs. 1-2 As shown, the present invention provides a technical solution, a smart host management system suitable for medium-voltage switchgear, the management system including an equipment reference degree evaluation module, a characteristic parameter effect data module, a target effect data module, a model optimization module, and a smart host management module;

[0073] The device reference degree evaluation module is configured to obtain device information of the medium-voltage cabinet and historical device use environment records of the historical medium-voltage cabinet, extract historical device use environment data from the historical device use environment records, evaluate a device reference degree of the historical medium-voltage cabinet to the medium-voltage cabinet, and obtain a reference historical medium-voltage cabinet.

[0074] The device reference degree evaluation module includes a device screening unit and a device reference degree evaluation unit.

[0075] The device screening unit is configured to construct a management cloud platform, obtain device information of the medium-voltage cabinet, and obtain device information of each historical medium-voltage cabinet from the cloud platform. The device information includes a device model of the medium-voltage cabinet. The historical medium-voltage cabinets are screened based on the device model of the medium-voltage cabinet, and historical medium-voltage cabinets with the same device model as the medium-voltage cabinet are retained to obtain a plurality of historical medium-voltage cabinets.

[0076] The device reference degree evaluation unit is configured to obtain historical device use environment records of the medium-voltage cabinet and the historical medium-voltage cabinet, calculate a marked device reference degree of the historical medium-voltage cabinet to the medium-voltage cabinet, and evaluate a device reference degree of the historical medium-voltage cabinet to the medium-voltage cabinet based on the marked device reference degree to obtain a reference historical medium-voltage cabinet of the medium-voltage cabinet.

[0077] The device reference degree evaluation unit includes:

[0078] The historical device use environment records of the medium-voltage cabinet are obtained, and historical device use environment data is extracted from the historical device use environment records. The historical device use environment data includes data corresponding to each environmental parameter.

[0079] For example, the environmental parameters include environmental temperature and environmental humidity.

[0080] The historical device use environment records of the plurality of historical medium-voltage cabinets are obtained, and historical device use environment data is obtained from the historical device use environment records.

[0081] The maximum and minimum values of the average values of each environmental parameter in each historical device use environment record of the medium-voltage cabinet are obtained, and characteristic values of each environmental parameter in the medium-voltage cabinet are calculated. The characteristic value B a of the a th environmental parameter in the medium-voltage cabinet is calculated as follows: a

[0082]

[0083] wherein n represents the total number of the historical device use environment records of the medium-voltage cabinet; C a,i represents the average value of the a th environmental parameter in the i th historical device use environment record; and a represents the average value of the a th environmental parameter in each historical device use record of the medium-voltage cabinet.​​a,max , C a,min respectively represent the maximum value C a,max of the average value of the a-th environmental parameter in each historical equipment use environment record of the medium voltage cabinet a,min ;

[0084] For example, the maximum value C 1,max of the average value of the 1st environmental parameter in each historical equipment use environment record of the medium voltage cabinet is 50, the minimum value C 1,min is 2; the total number n of the historical equipment use environment records of the medium voltage cabinet is 4; C 1,1 represents 20; C 1,2 represents 30; C 1,3 represents 2; C 1,4 represents 50; μ1 represents 26;

[0085] The characteristic value B1 of the 1st environmental parameter in the medium voltage cabinet is calculated as follows:

[0086] ,

[0087] The equipment reference degree of the medium voltage cabinet is evaluated by a plurality of historical medium voltage cabinets, and the specific process of evaluating the equipment reference degree of the medium voltage cabinet by the d-th historical medium voltage cabinet is as follows:

[0088] The characteristic values of the environmental parameters in the d-th historical medium voltage cabinet are obtained, and the marked equipment reference degree E d of the d-th historical medium voltage cabinet to the medium voltage cabinet is calculated:

[0089] ,

[0090] wherein m represents the total number of the environmental parameters of the medium voltage cabinet; B x represents the characteristic value of the x-th environmental parameter in the medium voltage cabinet; B d x represents the characteristic value of the x-th environmental parameter in the d-th historical medium voltage cabinet;

[0091] When the marked equipment reference degree E d is greater than a preset marked equipment reference degree threshold, it is determined that the d-th historical medium voltage cabinet has equipment reference value to the medium voltage cabinet, and the d-th historical medium voltage cabinet is recorded as a reference historical medium voltage cabinet of the medium voltage cabinet.

[0092] The characteristic parameter action data module is used to obtain the historical operation record of the reference historical medium voltage cabinet of the medium voltage cabinet, obtain historical operation data from the historical operation record, evaluate the action degree between different equipment operation parameters in the reference historical medium voltage cabinet, and obtain characteristic parameter action data.

[0093] The characteristic parameter action data module includes a characteristic parameter action data unit.

[0094] The characteristic parameter action data unit is configured to acquire each reference historical medium-voltage cabinet, acquire historical operation records of the reference historical medium-voltage cabinet, evaluate the action degree between each equipment operation parameter in the reference historical medium-voltage cabinet, and obtain characteristic parameter action data of the reference historical medium-voltage cabinet.

[0095] For example, the equipment operation parameters include equipment voltage, equipment current, equipment frequency, and the like.

[0096] The characteristic parameter action data unit includes:

[0097] The characteristic parameter action data unit is configured to acquire each reference historical medium-voltage cabinet, acquire historical operation records of the reference historical medium-voltage cabinet, acquire historical operation data from the historical operation records, and the historical operation data includes data corresponding to each equipment operation parameter in the reference historical medium-voltage cabinet.

[0098] The characteristic parameter action data unit is configured to evaluate the action degree between each equipment operation parameter in the reference historical medium-voltage cabinet, and the specific process of evaluating the action degree of the fth equipment operation parameter on the gth equipment operation parameter in the reference historical medium-voltage cabinet is as follows:

[0099] The characteristic parameter action data unit is configured to acquire the mean value of the fth equipment operation parameter in each historical operation record of the reference historical medium-voltage cabinet, and construct a parameter action regression model y g :

[0100] ,

[0101] wherein U0 represents an intercept; U f represents a regression coefficient of the fth equipment operation parameter in the reference historical medium-voltage cabinet; and epsilon represents an error term.

[0102] The characteristic parameter action data unit is configured to divide each historical operation record in the reference historical medium-voltage cabinet into a training set and a test set based on a preset division ratio, calculate a characteristic regression error S g of the parameter action regression model y g :

[0103] ,

[0104] wherein beta represents the total number of historical equipment operation records of the reference historical medium-voltage cabinet in the training set; y g,q represents the actual value of the mean value of the gth equipment operation parameter in the qth historical equipment operation record of the reference historical medium-voltage cabinet in the training set; and y 、 g,qThe parameter action regression model y g The predicted value of the mean value of the gth equipment operation parameter in the qth historical equipment operation record of the reference historical medium voltage cabinet;

[0105] The feature regression error S g The parameter action regression model y g is obtained by minimizing

[0106] ,

[0107] wherein x f,1 , x f,2 ,..., x f,β respectively represent the mean value of the fth equipment operation parameter in the 1st, 2nd,..., βth historical equipment operation record of the reference historical medium voltage cabinet in the training set;

[0108] The data vector Y in the parameter action regression model y g is obtained:

[0109] ,

[0110] wherein y g,1 , y g,2 ,..., y g,β respectively represent the mean value of the gth equipment operation parameter in the 1st, 2nd,..., βth historical equipment operation record of the reference historical medium voltage cabinet in the training set;

[0111] The vector U of regression coefficients in the parameter action regression model y g is obtained:

[0112] ,

[0113] wherein U = (U0, U f );

[0114] The root mean square error R g of the parameter action regression model y g is calculated using the test set:

[0115] ,

[0116] wherein ζ represents the total number of historical equipment operation records of the reference historical medium voltage cabinet in the test set; y △ g,z is the actual value of the mean value of the gth equipment operation parameter in the zth historical equipment operation record of the reference historical medium voltage cabinet in the test set; y △、 g,z The parameter action regression model y ga predicted value of a mean value of the gth equipment operation parameter in the zth historical equipment operation record;

[0117] When the root mean square error R g is less than a preset error threshold, it is determined that the parameter action regression model yg is constructed, and the parameter action regression model y g is obtained. f and is normalized.

[0118] The parameter action regression model y g is obtained. f When the absolute value of the regression coefficient U f is greater than a preset characteristic regression threshold U´, it is determined that the fth equipment operation parameter in the reference historical medium voltage cabinet has an action degree on the gth equipment operation parameter, and the fth equipment operation parameter is recorded as an action equipment operation parameter of the gth equipment operation parameter.

[0119] The action equipment operation parameters of the several equipment operation parameters in the reference historical medium voltage cabinet are obtained and collected to obtain the characteristic parameter action data of the reference historical medium voltage cabinet.

[0120] The target action data module is configured to calculate the characteristic action values between different equipment operation parameters in the medium voltage cabinet based on the characteristic parameter action data of the reference historical medium voltage cabinet of the medium voltage cabinet, analyze the action conditions between the equipment operation parameters in the medium voltage cabinet, and obtain target action data of the medium voltage cabinet.

[0121] The target action data module includes a characteristic action value unit and a target action data unit.

[0122] The characteristic action value unit is configured to obtain the characteristic parameter action data of each reference historical medium voltage cabinet of the medium voltage cabinet, and calculate the characteristic action values between the equipment operation parameters in the medium voltage cabinet.

[0123] The target action data unit is configured to analyze the action conditions between the equipment operation parameters in the medium voltage cabinet based on the characteristic action values, and obtain the target action data of the medium voltage cabinet.

[0124] The characteristic action value unit includes:

[0125] The characteristic parameter action data of each reference historical medium voltage cabinet of the medium voltage cabinet is obtained, and the action operation parameters corresponding to the equipment operation parameters in the reference historical medium voltage cabinet are obtained from the characteristic parameter action data.

[0126] The characteristic action values between the equipment operation parameters in the medium voltage cabinet are calculated, and the calculation process of the characteristic action value K h,v of the hth equipment operation parameter on the vth equipment operation parameter in the medium voltage cabinet is as follows:

[0127] the hth equipment operation parameter in the characteristic parameter action data is the characteristic action value K of the vth equipment operation parameter in the reference history of the action equipment operation parameter of the medium voltage switchgear h,v The specific calculation formula is:

[0128] ,

[0129] Wherein, δ represents the total number of reference history medium voltage switchgears for medium voltage switchgears, and δ is greater than the preset characteristic quantity threshold value; U h v,α The hth equipment operation parameter in the reference history of the medium voltage switchgear is the regression coefficient of the vth equipment operation parameter in the parameter action regression model of the vth equipment operation parameter;

[0130] Wherein, the target action data unit comprises:

[0131] When the characteristic action value K h,v Is greater than the first characteristic action threshold K1, it is determined that the hth equipment operation parameter in the medium voltage switchgear has a positive effect on the vth equipment operation parameter, and the hth equipment operation parameter is recorded as the positive effect equipment operation parameter of the vth equipment operation parameter;

[0132] When the characteristic action value K h,v Is less than the second characteristic action threshold K2, it is determined that the hth equipment operation parameter in the medium voltage switchgear has a negative effect on the vth equipment operation parameter, and the hth equipment operation parameter is recorded as the negative effect equipment operation parameter of the vth equipment operation parameter, wherein K1>0>K2;

[0133] The target action data of the medium voltage switchgear is obtained by collecting the several equipment operation parameters with positive effect equipment operation parameters and negative effect equipment operation parameters of the medium voltage switchgear;

[0134] The model optimization module is used to obtain the target action data of the medium voltage switchgear, obtain the historical fault operation record of the medium voltage switchgear, and optimize the preset fault detection model of the medium voltage switchgear.

[0135] Wherein, the model optimization module comprises a model optimization unit.

[0136] The model optimization unit is configured to acquire target action data of the medium-voltage cabinet, acquire historical fault operation records of the medium-voltage cabinet, acquire a preset fault detection model of the medium-voltage cabinet from a cloud platform, acquire a plurality of equipment operation parameters of the medium-voltage cabinet, the plurality of equipment operation parameters including positive equipment operation parameters and negative equipment operation parameters, acquire characteristic action values between the plurality of equipment operation parameters and the positive equipment operation parameters or the negative equipment operation parameters, input the characteristic action values into the preset fault detection model, and optimize the fault detection model in combination with the historical fault operation records of the medium-voltage cabinet.

[0137] The intelligent host management module is configured to use the intelligent host to monitor equipment operation states of the medium-voltage cabinet and generate equipment evaluation data, upload the equipment evaluation data to a mobile communication device of a user through the intelligent host, and manage the medium-voltage cabinet intelligently according to an instruction of the user on the mobile communication device.

[0138] For example, the mobile communication device includes a mobile phone, a tablet computer, and the like.

[0139] The intelligent host management module includes an intelligent host management unit.

[0140] The intelligent host management unit is configured to use the intelligent host to monitor equipment operation states of the medium-voltage cabinet in a current period and input data monitored by the intelligent host into the fault detection model, generate equipment evaluation data of the medium-voltage cabinet by the fault detection model, the equipment evaluation data including a device fault probability of the medium-voltage cabinet and data corresponding to each equipment operation parameter, upload the equipment evaluation data to a mobile communication device of a user through the intelligent host, and manage the medium-voltage cabinet intelligently according to an instruction of the user on the mobile communication device.

[0141] It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments but can be implemented in other embodiments without departing from the scope of the application. Thus, the scope of the application should be determined by the appended claims and not by the above description, which should be regarded as exemplary against which various modifications can be made without departing from the scope of the application. No reference signs in the claims should be considered as limiting the scope of the claims in any way.

Claims

1. A smart host management system suitable for medium-voltage switchgear, characterized in that, The management system includes a device reference level assessment module, a feature parameter effect data module, a target effect data module, a model optimization module, and a smart host management module; The equipment reference degree assessment module is used to acquire equipment information and historical equipment usage environment records in medium-voltage switchgear and historical medium-voltage switchgear, extract historical equipment usage environment data from the historical equipment usage environment records, assess the equipment reference degree of the historical medium-voltage switchgear to the medium-voltage switchgear, and obtain the reference historical medium-voltage switchgear. The feature parameter effect data module is used to acquire the historical operation records of the reference historical medium-voltage switchgear, obtain historical operation data from the historical operation records, evaluate the degree of interaction between different equipment operation parameters in the reference historical medium-voltage switchgear, and obtain feature parameter effect data. The target action data module is used to calculate the characteristic action values ​​between different equipment operating parameters in the medium-voltage switchgear based on the reference historical characteristic parameter action data of the medium-voltage switchgear, analyze the interaction between the equipment operating parameters in the medium-voltage switchgear, and obtain the target action data of the medium-voltage switchgear. The model optimization module is used to acquire the target action data of the medium-voltage switchgear, acquire the historical fault operation records of the medium-voltage switchgear, and optimize the preset fault detection model of the medium-voltage switchgear. The intelligent host management module is used to monitor the equipment operating status of the medium-voltage cabinet using the intelligent host, generate equipment evaluation data, upload the equipment evaluation data to the user's mobile communication device through the intelligent host, and perform intelligent equipment management of the medium-voltage cabinet according to the user's instructions on the mobile communication device. The equipment reference level assessment module includes an equipment screening unit and an equipment reference level assessment unit; The equipment screening unit is used to build a management cloud platform to acquire equipment information of medium-voltage switchgear. The equipment information includes the equipment model of the medium-voltage switchgear. The unit acquires equipment information of each historical medium-voltage switchgear from the cloud platform, filters the historical medium-voltage switchgear based on the equipment model of the medium-voltage switchgear, and retains the historical medium-voltage switchgear with the same equipment model as the medium-voltage switchgear to obtain several historical medium-voltage switchgear. The equipment reference degree evaluation unit is used to acquire historical equipment usage environment records of the medium-voltage switchgear and historical medium-voltage switchgear, calculate the marked equipment reference degree of the historical medium-voltage switchgear to the medium-voltage switchgear, and evaluate the equipment reference degree of the historical medium-voltage switchgear to the medium-voltage switchgear based on the marked equipment reference degree, so as to obtain the reference historical medium-voltage switchgear of the medium-voltage switchgear. The equipment reference level assessment unit includes: The historical equipment usage environment records of the medium-voltage switchgear are acquired, and historical equipment usage environment data is extracted from the historical equipment usage environment records. The historical equipment usage environment data includes data corresponding to various environmental parameters. Obtain historical equipment usage environment records for the aforementioned number of historical medium-voltage switchgear, and extract historical equipment usage environment data from the historical equipment usage environment records; Obtain the maximum and minimum values ​​of the average values ​​of each environmental parameter from the historical equipment usage environment records of the medium-voltage switchgear, and calculate the characteristic values ​​of each environmental parameter in the medium-voltage switchgear, wherein the characteristic value B of the a-th environmental parameter in the medium-voltage switchgear is... a : ， Where n represents the total number of historical equipment usage environment records for the medium-voltage switchgear; C a,i μ represents the average value of the a-th environmental parameter in the i-th historical device usage environment record; a This is represented as the average value of the environmental parameter in item a from the various historical device usage records described above; C a,max C a,min Each of the above-mentioned medium-voltage switchgear represents the maximum value C of the average value of the environmental parameter in item a, which is recorded in the historical equipment usage environment records of each device. a,max Minimum value C a,min ; The process of evaluating the equipment reference degree of the aforementioned historical medium-voltage switchgear to the medium-voltage switchgear, specifically the process of evaluating the equipment reference degree of the d-th historical medium-voltage switchgear to the medium-voltage switchgear, is as follows: Obtain the characteristic values ​​of various environmental parameters in the d-th historical medium-voltage switchgear, and calculate the reference degree E of the d-th historical medium-voltage switchgear to the marked equipment of the medium-voltage switchgear. d : ， Where m represents the total number of environmental parameters of the medium-pressure switchgear; B x B is represented by the characteristic value of the x-th environmental parameter in the medium-voltage switchgear; d x This is represented as the characteristic value of the x-th environmental parameter in the d-th historical medium-voltage switchgear; When the reference level of the marking device is E d If the value is greater than the preset threshold for the reference level of the marked equipment, it is determined that the d-th historical medium-voltage switchgear has equipment reference value for the medium-voltage switchgear, and the d-th historical medium-voltage switchgear is recorded as the reference historical medium-voltage switchgear of the medium-voltage switchgear.

2. The intelligent host management system for medium-voltage switchgear according to claim 1, characterized in that, The feature parameter effect data module includes a feature parameter effect data unit; The characteristic parameter effect data unit is used to acquire each reference historical medium-voltage cabinet of the medium-voltage cabinet, acquire the historical operation record of the reference historical medium-voltage cabinet, evaluate the degree of interaction between the various equipment operation parameters in the reference historical medium-voltage cabinet, and obtain the characteristic parameter effect data of the reference historical medium-voltage cabinet.

3. The intelligent host management system for medium-voltage switchgear according to claim 2, characterized in that, The feature parameter action data unit includes: The historical reference medium-voltage switchgear is acquired for each of the medium-voltage switchgear references, and the historical operation records of the reference historical medium-voltage switchgear are acquired. The historical operation data is obtained from the historical operation records, and the historical operation data includes the data corresponding to the operation parameters of each equipment in the reference historical medium-voltage switchgear references. The process of evaluating the interaction between various equipment operating parameters in the reference historical medium-voltage switchgear, specifically evaluating the effect of the f-th equipment operating parameter in the reference historical medium-voltage switchgear on the g-th equipment operating parameter, is as follows: Obtain the mean value of the f-th equipment operating parameter from each historical operation record of the reference historical medium-voltage switchgear, and construct a parameter interaction regression model y between the f-th equipment operating parameter and the g-th equipment operating parameter in the reference historical medium-voltage switchgear. g : ， Where U0 represents the intercept; U f ε represents the regression coefficient of the f-th equipment operating parameter in the reference historical medium-voltage switchgear; ε is the error term. Based on a preset division ratio, the historical operation records of each medium-voltage switchgear in the reference history are divided into a training set and a test set. Using the training set, the parameter effect regression model y is calculated. g The characteristic regression error S g : ， Where β is the total number of historical equipment operation records of the reference history switchgear in the training set; y g,q The reference historical medium-voltage switchgear is defined as the actual value of the average value of the g-th equipment operating parameter within the q-th historical equipment operation record in the training set; y 、 g,q The parameters are applied to the regression model y g The predicted value of the average value of the g-th equipment operation parameter within the q-th historical equipment operation record; The feature regression error S g Minimize, and obtain the effect of the parameters on the regression model y g Design matrix X: ， Where, x f,1 x f,2 ... x f,β These represent the average values ​​of the f-th equipment operating parameter in the 1st, 2nd, ..., βth historical equipment operation records of the switchgear in the training set; Obtain the effect of the parameters on the regression model y g Data vector Y in: ， Among them, y g,1 y g,2 ... y g,β These represent the average values ​​of the g-th equipment operating parameter in the 1st, 2nd, ..., βth historical equipment operation records of the switchgear in the training set; Obtaining parameters for the regression model y g The vector U of regression coefficients in: ， Among them, U = (U0, U f ); Using the test set, calculate the parameter effect regression model y. g Root mean square error R g : ， Where ζ represents the total number of historical equipment operation records of the reference historical medium-voltage switchgear in the test set; y △ g,z The reference historical medium-voltage switchgear is defined as the actual value of the average value of the g-th equipment operating parameter within the z-th historical equipment operation record in the test set; y △、 g,z The parameters are applied to the regression model y g The predicted value of the mean value of the g-th equipment operation parameter within the z-th historical equipment operation record; When the root mean square error R g If the error is less than a preset error threshold, the parameter effect regression model yg is determined to be successfully constructed, and the parameter effect regression model yg is obtained. g In the above, the regression coefficient U of the f-th equipment operating parameter f And standardize the process; Obtain the effect of the parameters on the regression model y g The regression coefficient U in f When the regression coefficient U f If the absolute value of the parameter is greater than the preset feature regression threshold U´, determine the degree of influence of the f-th equipment operating parameter in the reference history switchgear on the g-th equipment operating parameter, and record the f-th equipment operating parameter as the operating parameter of the g-th equipment operating parameter. The operating parameters of several equipment operating parameters in the reference historical medium-voltage switchgear are obtained and aggregated to obtain the characteristic parameter data of the reference historical medium-voltage switchgear.

4. The intelligent host management system for medium-voltage switchgear according to claim 2, characterized in that, The target action data module includes a feature action value unit and a target action data unit; The characteristic action value unit is used to acquire the characteristic parameter action data of each reference historical medium-voltage switchgear in the medium-voltage switchgear, and calculate the characteristic action value between the various equipment operating parameters in the medium-voltage switchgear; The target action data unit is used to analyze the interaction between the equipment operating parameters in the medium-voltage switchgear based on the characteristic action value, and to obtain the target action data of the medium-voltage switchgear.

5. A smart host management system suitable for medium-voltage switchgear according to claim 4, characterized in that, The characteristic action value unit includes: Obtain the characteristic parameter action data of each reference historical medium-voltage switchgear, and obtain the action operation parameters corresponding to the equipment operation parameters in the reference historical medium-voltage switchgear from the characteristic parameter action data; Calculate the characteristic interaction values ​​between the various equipment operating parameters in the medium-voltage switchgear, wherein the characteristic interaction value K of the h-th equipment operating parameter in the medium-voltage switchgear on the v-th equipment operating parameter is... h,v The calculation process is as follows: The h-th equipment operating parameter in the characteristic parameter effect data is obtained, which is a number of reference historical medium-voltage switchgear for the equipment operating parameters of the v-th equipment operating parameter, and the characteristic effect value K is... h,v The specific calculation formula is as follows: ， Wherein, δ represents the total number of medium-voltage switchgear in the reference history, and δ is greater than a preset feature quantity threshold; U h v,α Let be the regression coefficient of the h-th equipment operating parameter in the α-th reference historical medium-voltage switchgear, which affects the v-th equipment operating parameter within the parameter effect regression model.

6. A smart host management system suitable for medium-voltage switchgear according to claim 5, characterized in that, The target action data unit includes: When the characteristic action value K h,v If the value is greater than the preset first feature action threshold K1, it is determined that the h-th equipment operating parameter in the medium-voltage cabinet has a positive effect on the v-th equipment operating parameter. The h-th equipment operating parameter is recorded as the positive effect equipment operating parameter of the v-th equipment operating parameter. When the characteristic action value K h,v If the value is less than the preset second feature effect threshold K2, it is determined that the h-th equipment operating parameter in the medium-voltage cabinet has a negative effect on the v-th equipment operating parameter. The h-th equipment operating parameter is recorded as the negative effect equipment operating parameter of the v-th equipment operating parameter, where K1>0>K2. Several equipment operating parameters of the medium-voltage switchgear with positive and negative effects are obtained and aggregated to obtain the target effect data of the medium-voltage switchgear.

7. A smart host management system suitable for medium-voltage switchgear according to claim 6, characterized in that, The model optimization module includes a model optimization unit; The model optimization unit is used to acquire the target action data of the medium-voltage switchgear, acquire the historical fault operation records of the medium-voltage switchgear, acquire the preset fault detection model of the medium-voltage switchgear from the cloud platform, acquire several equipment operation parameters of the medium-voltage switchgear with positive and negative action equipment operation parameters, acquire the characteristic action values ​​between the several equipment operation parameters and the positive or negative action equipment operation parameters, input the characteristic action values ​​into the preset fault detection model, and optimize the fault detection model in combination with the historical fault operation records of the medium-voltage switchgear.

8. A smart host management system suitable for medium-voltage switchgear according to claim 7, characterized in that, The intelligent host management module includes an intelligent host management unit; The intelligent host management unit is used to monitor the equipment operating status of the medium-voltage cabinet within the current period using the intelligent host, and input the data monitored by the intelligent host into the fault detection model. The fault detection model generates equipment evaluation data for the medium-voltage cabinet, which includes the equipment failure probability of the medium-voltage cabinet and data corresponding to various equipment operating parameters. The intelligent host uploads the equipment evaluation data to the user's mobile communication device, and the intelligent host performs intelligent equipment management of the medium-voltage cabinet according to the instructions of the user on the mobile communication device.

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