Smart host management system suitable for medium-voltage cabinet

Abstract

The invention discloses a smart host management system suitable for a medium-voltage cabinet, and relates to the technical field of medium-voltage cabinet management, and the system comprises an equipment reference degree evaluation module, a feature parameter action data module, a target action data module, a model optimization module, and a smart host management module. The equipment reference degree evaluation module is used for evaluating the equipment reference degree of the historical medium-voltage cabinet to the medium-voltage cabinet; the characteristic parameter action data module is used for evaluating the action degree between different equipment operation parameters in the voltage cabinet in the parameter history; the target action data module is used for analyzing action conditions among equipment operation parameters in the medium-voltage cabinet; the model optimization module is used for optimizing the fault detection model; and the smart host management module is used for uploading the equipment evaluation data to mobile communication equipment of a user through a smart host, and the smart host performs smart equipment management on the medium-voltage cabinet according to an instruction of the user on the mobile communication equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of medium voltage cabinet management, and in particular to an intelligent host management system suitable for medium voltage cabinets. Background Art

[0002] The medium voltage cabinet is a kind of equipment in the power system, which is mainly used for the distribution and control of medium voltage power. It usually converts high voltage into medium voltage and plays the role of distribution and protection in the medium voltage power grid. At present, the main method for equipment management of the medium voltage cabinet is to use sensors to monitor the equipment parameters such as voltage, current, temperature, etc. of the medium voltage cabinet, and analyze the equipment status of the medium voltage cabinet according to the upper and lower limit thresholds set by the equipment parameters of the normal operation of the medium voltage cabinet. When the measured value corresponding to the equipment parameter collected in real time exceeds the preset range, an alarm is triggered. However, in actual situations, the equipment model of the medium voltage cabinet, the equipment use environment, and the mutual influence between different equipment parameters all cause the actual normal operation range of the equipment parameters to change. At present, users cannot use mobile communication devices to grasp and control the equipment status of the medium voltage cabinet in real time. These situations will not only make the judgment of the equipment status of the medium voltage cabinet wrong, but also make it impossible for users to grasp the equipment status of the medium voltage cabinet in time, which will not only cause equipment damage to the medium voltage cabinet, but also may cause serious safety accidents, resulting in significant losses. Summary of the invention

[0003] The object of the present invention is to provide an intelligent host management system suitable for medium voltage cabinets to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: a smart host management system suitable for medium voltage cabinets, the management system includes 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;

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

[0006] The characteristic parameter effect data module is used to obtain the historical operation records of the reference history medium voltage cabinet of the medium voltage cabinet, obtain the historical operation data from the historical operation records, evaluate the degree of effect between the operation parameters of different equipment in the parameter history medium voltage cabinet, and obtain the characteristic parameter effect data;

[0007] The target action data module is used to calculate the characteristic action values ​​between the operating parameters of different equipment in the medium voltage cabinet based on the characteristic parameter action data of the reference history medium voltage cabinet, analyze the action between the equipment operating parameters in the medium voltage cabinet, and obtain the target action data of the medium voltage cabinet;

[0008] The model optimization module is used to obtain the target action data of the medium voltage cabinet, obtain the historical fault operation record of the medium voltage cabinet, and optimize the preset fault detection model of the medium voltage cabinet;

[0009] The smart host management module is used to use the smart host to monitor the equipment operation status of the medium voltage cabinet and generate equipment evaluation data. The equipment evaluation data is uploaded to the user's mobile communication device through the smart host. The smart host performs intelligent device management of the medium voltage cabinet according to the user's instructions on the mobile communication device.

[0010] Further, the device reference degree evaluation module includes a device screening unit and a device reference degree evaluation unit;

[0011] The device screening unit is used to build a management cloud platform to obtain the device information of the medium voltage cabinet, including the device model of the medium voltage cabinet, obtain the device information of each historical medium voltage cabinet from the cloud platform, and screen the historical medium voltage cabinets based on the device model of the medium voltage cabinet, and retain the historical medium voltage cabinets with the same device model as the medium voltage cabinet to obtain several historical medium voltage cabinets;

[0012] The equipment reference degree evaluation unit is used to obtain the historical equipment usage environment records of the medium voltage cabinet and the historical medium voltage cabinet, calculate the marked equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet, and evaluate the equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet based on the marked equipment reference degree to obtain the reference historical medium voltage cabinet of the medium voltage cabinet.

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

[0014] Acquire the historical equipment use environment records of the medium voltage cabinet, and extract the historical equipment use environment data from the historical equipment use environment records, wherein the historical equipment use environment data includes data corresponding to various environmental parameters;

[0015] Obtain historical equipment use environment records of several historical medium voltage cabinets, and obtain historical equipment use environment data from the historical equipment use environment records;

[0016] Obtain the maximum and minimum values ​​of the average values ​​of various environmental parameters in the historical equipment use environment records of the medium voltage cabinet, and calculate the characteristic values ​​of various environmental parameters in the medium voltage cabinet, among which the characteristic value B of the a-th environmental parameter in the medium voltage cabinet is a :

[0017]

[0018] Where n represents the total number of historical equipment usage environment records of the medium voltage cabinet; C a,i It is expressed as the average value of the ath environmental parameter in the ith historical equipment use environment record; μ a It is expressed as the mean of the average values ​​of the environmental parameters in item a in the historical equipment usage records; C a,max , C a,min They are respectively represented by the maximum value C of the average value of the environmental parameter of item a in the historical equipment use environment records of the medium voltage cabinet a,max , minimum value C a,min ;

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

[0020] Obtain the characteristic values ​​of various environmental parameters in the d-th historical medium voltage cabinet, and calculate the reference degree E of the marking device of the d-th historical medium voltage cabinet to the medium voltage cabinet d :

[0021]

[0022] Wherein, m represents the total number of environmental parameters of the medium voltage cabinet; B x Expressed as the characteristic value of the xth environmental parameter in the medium voltage cabinet; B d x It is expressed as the characteristic value of the xth environmental parameter in the dth historical medium voltage cabinet;

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

[0024] Further, the characteristic parameter action data module includes a characteristic parameter action data unit;

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

[0026] Furthermore, the characteristic parameter action data unit includes:

[0027] Obtain each reference historical medium voltage cabinet of the medium voltage cabinet, obtain the historical operation record of the reference historical medium voltage cabinet, obtain the historical operation data from the historical operation record, and the historical operation data includes data corresponding to the operation parameters of various equipment in the reference historical medium voltage cabinet;

[0028] The degree of influence between the equipment operating parameters in the parameter history medium voltage cabinet is evaluated. The specific process of evaluating the degree of influence of the equipment operating parameter item f in the parameter history medium voltage cabinet on the equipment operating parameter item g is as follows:

[0029] Obtain the mean value of the f-th equipment operating parameter in each historical operation record of the parameter history medium voltage cabinet, and construct the parameter effect regression model y of the f-th equipment operating parameter in the parameter history medium voltage cabinet and the g-th equipment operating parameter g :

[0030] y g =U0+U f ·x f +ε,

[0031] Among them, U0 represents the intercept; U f It is expressed as the regression coefficient of the fth equipment operating parameter in the parameter history medium voltage cabinet; ε is the error term;

[0032] Based on the preset division ratio, each historical operation record in the parameter history medium voltage cabinet is divided into a training set and a test set. The training set is used to calculate the parameter effect regression model y g The characteristic regression error S g :

[0033]

[0034] Among them, β is the total number of historical equipment operation records of the parameter history medium voltage cabinet in the training set; y g,q is the actual value of the mean value of the g-th equipment operation parameter in the q-th historical equipment operation record of the parameter history medium voltage cabinet in the training set; 、 g,q is the parameter effect regression model y g The predicted value of the mean value of the g-th equipment operation parameter in the q-th historical equipment operation record;

[0035] The feature regression error S g Minimize and obtain the parameter effect regression model y g The design matrix X is:

[0036]

[0037] Among them, x f,1 、x f,2 , ..., xf,β They are respectively represented as the mean values ​​of the fth equipment operation parameters in the 1st, 2nd, ..., βth historical equipment operation records of the parameter history medium voltage cabinet in the training set;

[0038] Get parameter effect regression model y g The data vector Y is:

[0039]

[0040] Among them, y g,1 ,y g,2 ,...,y g,β They are respectively represented as the mean values ​​of the g-th equipment operation parameters in the 1st, 2nd, ..., βth historical equipment operation records of the parameter history medium voltage cabinet in the training set;

[0041] Get parameter effect regression model y g The vector U of regression coefficients in :

[0042] U=(X T ·X) -1 ·X T Y,

[0043] Among them, U=(U0,U f );

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

[0045]

[0046] Where ζ represents the total number of historical equipment operation records of the parameter history medium voltage cabinet in the test set; y △ g,z is the actual value of the mean value of the g-th equipment operation parameter in the z-th historical equipment operation record of the parameter history medium voltage cabinet in the test set; △、 g,z is the parameter effect regression model y g The predicted value of the mean value of the g-th equipment operation parameter in the z-th historical equipment operation record;

[0047] When the root mean square error R g If the error is less than the preset error threshold, the construction of the parameter effect regression model yg is determined to be completed, and the parameter effect regression model y is obtained. g The regression coefficient U of the fth equipment operating parameter is f And standardize it;

[0048] Get parameter effect regression model y g The regression coefficient Uf , when the regression coefficient U f The absolute value of is greater than the preset characteristic regression threshold value U′, and the effect degree of the f-th equipment operation parameter in the parameter history medium voltage cabinet on the g-th equipment operation parameter is determined, and the f-th equipment operation parameter is recorded as the effect equipment operation parameter of the g-th equipment operation parameter;

[0049] The function equipment operation parameters of several equipment operation parameters in the parameter history medium voltage cabinet are obtained and collected to obtain the characteristic parameter function data of the parameter history medium voltage cabinet.

[0050] Further, the target action data module includes a characteristic action value unit and a target action data unit;

[0051] The characteristic action value unit is used to obtain the characteristic parameter action data of each reference history medium voltage cabinet of the medium voltage cabinet, and calculate the characteristic action value between the operating parameters of various equipment in the medium voltage cabinet;

[0052] The target action data unit is used to analyze the action between the equipment operating parameters in the medium voltage cabinet according to the characteristic action value, and obtain the target action data of the medium voltage cabinet.

[0053] Furthermore, the characteristic action value unit includes:

[0054] Obtaining characteristic parameter action data of each reference history medium voltage cabinet of the medium voltage cabinet, and obtaining action operation parameters corresponding to the equipment operation parameters in the reference history medium voltage cabinet from the characteristic parameter action data;

[0055] Calculate the characteristic action value between the operating parameters of each device in the medium voltage cabinet, where the characteristic action value K of the hth equipment operating parameter on the vth equipment operating parameter is h,v The calculation process is:

[0056] Get the hth item of equipment operating parameters in the characteristic parameter action data, which is several reference historical medium voltage cabinets of the equipment operating parameters of the vth item of equipment operating parameters, and the characteristic action value K h,v The specific calculation formula is:

[0057]

[0058] Wherein, δ represents the total number of reference history medium voltage cabinets of the medium voltage cabinet, and δ is greater than the preset feature quantity threshold; U h v,α It is expressed as the regression coefficient of the vth equipment operating parameter in the parameter effect regression model of the hth equipment operating parameter in the αth reference history medium voltage cabinet on the vth equipment operating parameter.

[0059] Furthermore, the target action data unit includes:

[0060] When the characteristic action value K h,v is greater than a preset first characteristic action threshold value 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, and the h-th equipment operating parameter is recorded as the positive effect equipment operating parameter of the v-th equipment operating parameter;

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

[0062] The equipment operating parameters of the medium voltage cabinet with positive effect equipment operating parameters and several equipment operating parameters of the negative effect equipment operating parameters are obtained and collected to obtain the target effect data of the medium voltage cabinet.

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

[0064] The model optimization unit is used 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 several equipment operation parameters of the medium voltage cabinet with positive action equipment operation parameters and negative action equipment operation parameters, obtain characteristic action values ​​between several equipment operation parameters and positive action equipment operation parameters, 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 record of the medium voltage cabinet.

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

[0066] The intelligent host management unit is used to monitor the equipment operation status of the medium voltage cabinet in the current cycle 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 of the medium voltage cabinet. The equipment evaluation data includes the equipment failure probability of the medium voltage cabinet and the data corresponding to various equipment operation parameters. The equipment evaluation data is uploaded to the user's mobile communication device through the intelligent host. The intelligent host performs intelligent equipment management of the medium voltage cabinet according to the user's instructions on the mobile communication device;

[0067] In the above steps, the smart host can not only monitor the equipment status of the medium voltage cabinet, but also be connected 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 facilitates the user's grasp of the medium voltage cabinet equipment information, but also greatly reduces the risk of failure of the medium voltage cabinet.

[0068] Compared with the prior art, the beneficial effects of the present invention are: the present invention realizes the intelligent management of the medium voltage cabinet, taking into account the equipment model, equipment use environment, and mutual influence between different equipment parameters of the medium voltage cabinet in actual situations, all of which cause changes in the actual normal operating range of the equipment parameters. First, a historical medium voltage cabinet with equipment reference for the medium voltage cabinet is obtained, and the interaction of the equipment operating parameters in the historical medium voltage cabinet is analyzed to determine the effect of the equipment operating parameters in the medium voltage cabinet, and optimize the fault detection model. At the same time, the equipment operating status of the medium voltage cabinet is monitored by the smart host, and the real-time detection data of the equipment is uploaded to the user's mobile communication device using the smart host. This not only allows the user to grasp the equipment status of the medium voltage cabinet in a timely manner, but also makes the results of the intelligent host's detection of the medium voltage cabinet more accurate, greatly reducing the probability of equipment failure in the medium voltage cabinet and reducing the possibility of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a unit schematic diagram of an intelligent host management system applicable to a medium voltage cabinet of the present invention;

[0070] Figure 2 It is a module schematic diagram of an intelligent host management system suitable for a medium voltage cabinet according to the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a smart host management system suitable for medium voltage cabinets, the management system includes 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 equipment reference degree evaluation module is used to obtain the equipment information and historical equipment use environment records in the medium voltage cabinet and the historical medium voltage cabinet, extract the historical equipment use environment data from the historical equipment use environment records, evaluate the equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet, and obtain the reference historical medium voltage cabinet;

[0074] Wherein, the equipment reference degree evaluation module includes an equipment screening unit and an equipment reference degree evaluation unit;

[0075] The device screening unit is used to build a management cloud platform to obtain the device information of the medium voltage cabinet, including the device model of the medium voltage cabinet, obtain the device information of each historical medium voltage cabinet from the cloud platform, and screen the historical medium voltage cabinets based on the device model of the medium voltage cabinet, and retain the historical medium voltage cabinets with the same device model as the medium voltage cabinet to obtain several historical medium voltage cabinets;

[0076] An equipment reference degree evaluation unit is used to obtain the historical equipment use environment records of the medium voltage cabinet and the historical medium voltage cabinet, calculate the marked equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet, and evaluate the equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet according to the marked equipment reference degree, so as to obtain the reference historical medium voltage cabinet of the medium voltage cabinet;

[0077] Among them, the equipment reference level assessment unit includes:

[0078] Acquire the historical equipment use environment records of the medium voltage cabinet, and extract the historical equipment use environment data from the historical equipment use environment records, wherein the historical equipment use environment data includes data corresponding to various environmental parameters;

[0079] For example, various environmental parameters include ambient temperature, ambient humidity, etc.;

[0080] Obtain historical equipment use environment records of several historical medium voltage cabinets, and obtain historical equipment use environment data from the historical equipment use environment records;

[0081] Obtain the maximum and minimum values ​​of the average values ​​of various environmental parameters in the historical equipment use environment records of the medium voltage cabinet, and calculate the characteristic values ​​of various environmental parameters in the medium voltage cabinet, among which the characteristic value B of the a-th environmental parameter in the medium voltage cabinet is a :

[0082]

[0083] Where n represents the total number of historical equipment usage environment records of the medium voltage cabinet; C a,i It is expressed as the average value of the ath environmental parameter in the ith historical equipment use environment record; μ a It is expressed as the mean of the average values ​​of the environmental parameters in item a in the historical equipment usage records; Ca,max , C a,min They are respectively represented by the maximum value C of the average value of the environmental parameter of item a in the historical equipment use environment records of the medium voltage cabinet a,max , minimum value C a,min ;

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

[0085] Calculate the characteristic value B1 of the first environmental parameter in the medium voltage cabinet:

[0086]

[0087] The equipment reference degree of several historical medium voltage cabinets to the medium voltage cabinet is evaluated, wherein the specific process of evaluating the equipment reference degree of the d-th historical medium voltage cabinet to the medium voltage cabinet is as follows:

[0088] Obtain the characteristic values ​​of various environmental parameters in the d-th historical medium voltage cabinet, and calculate the reference degree E of the marking device of the d-th historical medium voltage cabinet to the medium voltage cabinet d :

[0089]

[0090] Wherein, m represents the total number of environmental parameters of the medium voltage cabinet; B x Expressed as the characteristic value of the xth environmental parameter in the medium voltage cabinet; B d x It is expressed as the characteristic value of the xth environmental parameter in the dth historical medium voltage cabinet;

[0091] When marking the device reference level E d If the value is greater than a preset marking device reference level threshold, it is determined that the d-th historical medium voltage cabinet has a device reference value for the medium voltage cabinet, and the d-th historical medium voltage cabinet is recorded as a reference historical medium voltage cabinet for the medium voltage cabinet;

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

[0093] Wherein, the characteristic parameter action data module includes a characteristic parameter action data unit;

[0094] The characteristic parameter action data unit is used to obtain each reference history medium voltage cabinet of the medium voltage cabinet, obtain the historical operation record of the reference history medium voltage cabinet, evaluate the degree of action between various equipment operation parameters in the parameter history medium voltage cabinet, and obtain the characteristic parameter action data of the parameter history medium voltage cabinet;

[0095] For example, various equipment operating parameters include equipment voltage, equipment current, equipment frequency, etc.;

[0096] Among them, the characteristic parameter action data unit includes:

[0097] Obtain each reference historical medium voltage cabinet of the medium voltage cabinet, obtain the historical operation record of the reference historical medium voltage cabinet, obtain the historical operation data from the historical operation record, and the historical operation data includes data corresponding to the operation parameters of various equipment in the reference historical medium voltage cabinet;

[0098] The degree of influence between the equipment operating parameters in the parameter history medium voltage cabinet is evaluated. The specific process of evaluating the degree of influence of the equipment operating parameter item f in the parameter history medium voltage cabinet on the equipment operating parameter item g is as follows:

[0099] Obtain the mean value of the f-th equipment operating parameter in each historical operation record of the parameter history medium voltage cabinet, and construct the parameter effect regression model y of the f-th equipment operating parameter in the parameter history medium voltage cabinet and the g-th equipment operating parameter g :

[0100] y g =U0+U f ·x f +ε,

[0101] Among them, U0 represents the intercept; U f It is expressed as the regression coefficient of the fth equipment operating parameter in the parameter history medium voltage cabinet; ε is the error term;

[0102] Based on the preset division ratio, each historical operation record in the parameter history medium voltage cabinet is divided into a training set and a test set. The training set is used to calculate the parameter effect regression model y g The characteristic regression error S g :

[0103]

[0104] Among them, β is the total number of historical equipment operation records of the parameter history medium voltage cabinet in the training set; y g,q is the actual value of the mean value of the g-th equipment operation parameter in the q-th historical equipment operation record of the parameter history medium voltage cabinet in the training set;、 g,q is the parameter effect regression model y g The predicted value of the mean value of the g-th equipment operation parameter in the q-th historical equipment operation record;

[0105] The feature regression error S g Minimize and obtain the parameter effect regression model y g The design matrix X is:

[0106]

[0107] Among them, x f,1 、x f,2 , ..., x f,β They are respectively represented as the mean values ​​of the fth equipment operation parameters in the 1st, 2nd, ..., βth historical equipment operation records of the parameter history medium voltage cabinet in the training set;

[0108] Get parameter effect regression model y g The data vector Y is:

[0109]

[0110] Among them, y g,1 ,y g,2 ,...,y g,β They are respectively represented as the mean values ​​of the g-th equipment operation parameters in the 1st, 2nd, ..., βth historical equipment operation records of the parameter history medium voltage cabinet in the training set;

[0111] Get parameter effect regression model y g The vector U of regression coefficients in :

[0112] U=(X T ·X) -1 ·X T Y,

[0113] Among them, U=(U0,U f );

[0114] Using the test set, calculate the parameter effect regression model y g The root mean square error R g :

[0115]

[0116] Where ζ represents the total number of historical equipment operation records of the parameter history medium voltage cabinet in the test set; y △ g,z is the actual value of the mean value of the g-th equipment operation parameter in the z-th historical equipment operation record of the parameter history medium voltage cabinet in the test set; △、g,z is the parameter effect regression model y g The predicted value of the mean value of the g-th equipment operation parameter in the z-th historical equipment operation record;

[0117] When the root mean square error R g If the error is less than the preset error threshold, the construction of the parameter effect regression model yg is determined to be completed, and the parameter effect regression model y is obtained. g The regression coefficient U of the fth equipment operating parameter is f And standardize it;

[0118] Get parameter effect regression model y g The regression coefficient U f , when the regression coefficient U f The absolute value of is greater than the preset characteristic regression threshold value U′, and the effect degree of the f-th equipment operation parameter in the parameter history medium voltage cabinet on the g-th equipment operation parameter is determined, and the f-th equipment operation parameter is recorded as the effect equipment operation parameter of the g-th equipment operation parameter;

[0119] Obtain the function equipment operation parameters of several equipment operation parameters in the parameter history medium voltage cabinet and collect them to obtain the characteristic parameter function data of the parameter history medium voltage cabinet;

[0120] The target action data module is used to calculate the characteristic action values ​​between the operating parameters of different equipment in the medium voltage cabinet based on the characteristic parameter action data of the reference history medium voltage cabinet, analyze the action between the equipment operating parameters in the medium voltage cabinet, and obtain the target action data of the medium voltage cabinet;

[0121] Wherein, the target action data module includes a characteristic action value unit and a target action data unit;

[0122] The characteristic action value unit is used to obtain the characteristic parameter action data of each reference history medium voltage cabinet of the medium voltage cabinet, and calculate the characteristic action value between the operating parameters of various equipment in the medium voltage cabinet;

[0123] The target action data unit is used to analyze the action between the equipment operating parameters in the medium voltage cabinet according to the characteristic action value, and obtain the target action data of the medium voltage cabinet;

[0124] Among them, the characteristic action value unit includes:

[0125] Obtaining characteristic parameter action data of each reference history medium voltage cabinet of the medium voltage cabinet, and obtaining action operation parameters corresponding to the equipment operation parameters in the reference history medium voltage cabinet from the characteristic parameter action data;

[0126] Calculate the characteristic action value between the operating parameters of each device in the medium voltage cabinet, where the characteristic action value K of the hth equipment operating parameter on the vth equipment operating parameter is h,v The calculation process is:

[0127] Get the hth item of equipment operating parameters in the characteristic parameter action data, which is several reference historical medium voltage cabinets of the equipment operating parameters of the vth item of equipment operating parameters, and the characteristic action value K h,v The specific calculation formula is:

[0128]

[0129] Wherein, δ represents the total number of reference history medium voltage cabinets of the medium voltage cabinet, and δ is greater than the preset feature quantity threshold; U h v,α It is represented as the regression coefficient of the hth equipment operating parameter in the αth reference history medium voltage cabinet on the vth equipment operating parameter in the parameter effect regression model;

[0130] Among them, the target action data unit includes:

[0131] When the characteristic action value K h,v is greater than a preset first characteristic action threshold value 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, and the h-th equipment operating parameter is recorded as the positive effect equipment operating parameter of the v-th equipment operating parameter;

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

[0133] Obtaining and aggregating several equipment operation parameters of the medium voltage cabinet having positive effect equipment operation parameters and negative effect equipment operation parameters to obtain target effect data of the medium voltage cabinet;

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

[0135] Wherein, the model optimization module includes a model optimization unit;

[0136] The model optimization unit is used 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 several equipment operation parameters of the medium voltage cabinet with positive action equipment operation parameters and negative action equipment operation parameters, obtain characteristic action values ​​between several equipment operation parameters and positive action equipment operation parameters, 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 record of the medium voltage cabinet;

[0137] The smart host management module is used to use the smart host to monitor the equipment operation status of the medium voltage cabinet and generate equipment evaluation data. The equipment evaluation data is uploaded to the user's mobile communication device through the smart host. The smart host performs intelligent equipment management of the medium voltage cabinet according to the user's instructions on the mobile communication device;

[0138] For example, mobile communication devices include mobile phones, tablets, etc.;

[0139] Among them, the intelligent host management module includes an intelligent host management unit;

[0140] The intelligent host management unit is used to monitor the equipment operation status of the medium voltage cabinet in the current cycle using the smart host, and input the data monitored by the smart host into the fault detection model. The fault detection model generates equipment evaluation data of the medium voltage cabinet. The equipment evaluation data includes the equipment failure probability of the medium voltage cabinet and the data corresponding to various equipment operating parameters. The equipment evaluation data is uploaded to the user's mobile communication device through the smart host. The smart host performs intelligent device management of the medium voltage cabinet according to the user's instructions on the mobile communication device.

[0141] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent host management system suitable for medium voltage cabinets, characterized in that: The management system includes an equipment reference degree evaluation module, a characteristic parameter effect data module, a target effect data module, a model optimization module, and an intelligent host management module; The equipment reference degree evaluation module is used to obtain equipment information and historical equipment use environment records in the medium voltage cabinet and the historical medium voltage cabinet, extract historical equipment use environment data from the historical equipment use environment records, evaluate the equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet, and obtain a reference historical medium voltage cabinet; The characteristic parameter effect data module is used to obtain the historical operation records of the reference history medium voltage cabinet of the medium voltage cabinet, obtain the historical operation data from the historical operation records, evaluate the degree of effect between the operation parameters of different equipment in the parameter history medium voltage cabinet, and obtain the characteristic parameter effect data; The target action data module is used to calculate the characteristic action values ​​between the operating parameters of different equipment in the medium voltage cabinet based on the characteristic parameter action data of the reference history medium voltage cabinet, analyze the action between the equipment operating parameters in the medium voltage cabinet, and obtain the target action data of the medium voltage cabinet; The model optimization module is used to obtain the target action data of the medium voltage cabinet, obtain the historical fault operation record of the medium voltage cabinet, and optimize the preset fault detection model of the medium voltage cabinet; The smart host management module is used to use the smart host to monitor the equipment operation status of the medium voltage cabinet and generate equipment evaluation data. The equipment evaluation data is uploaded to the user's mobile communication device through the smart host. The smart host performs intelligent device management of the medium voltage cabinet according to the instructions of the user on the mobile communication device.

2. According to claim 1, a smart host management system suitable for medium voltage cabinets is characterized in that: The equipment reference degree evaluation module includes an equipment screening unit and an equipment reference degree evaluation unit; The device screening unit is used to build a management cloud platform, obtain the device information of the medium voltage cabinet, the device information includes the device model of the medium voltage cabinet, obtain the device information of each historical medium voltage cabinet from the cloud platform, and screen the historical medium voltage cabinets based on the device model of the medium voltage cabinet, and retain the historical medium voltage cabinets with the same device model as the medium voltage cabinet to obtain a plurality of historical medium voltage cabinets; The equipment reference degree evaluation unit is used to obtain the historical equipment usage environment records of the medium voltage cabinet and the historical medium voltage cabinet, calculate the marked equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet, and evaluate the equipment reference degree of the historical medium voltage cabinet to the medium voltage cabinet based on the marked equipment reference degree, so as to obtain the reference historical medium voltage cabinet of the medium voltage cabinet.

3. The intelligent host management system applicable to medium voltage cabinets according to claim 2 is characterized in that: The equipment reference level evaluation unit comprises: Acquiring historical equipment use environment records of the medium voltage cabinet, and extracting historical equipment use environment data from the historical equipment use environment records, wherein the historical equipment use environment data includes data corresponding to various environmental parameters; Obtaining historical equipment usage environment records of the plurality of historical medium voltage cabinets, and obtaining historical equipment usage environment data from the historical equipment usage environment records; Obtain the maximum and minimum values ​​of the average values ​​of the various environmental parameters in each historical equipment use environment record of the medium voltage cabinet, and calculate the characteristic values ​​of the various environmental parameters in the medium voltage cabinet, wherein the characteristic value B of the a-th environmental parameter in the medium voltage cabinet a : Wherein, n represents the total number of the historical equipment usage environment records of the medium voltage cabinet; C a,i It is expressed as the average value of the environmental parameter item a in the i-th historical equipment use environment record; μ a It is represented by the average value of the average value of the environmental parameter in item a in each of the historical equipment usage records; C a,max , C a,min They are respectively represented by the maximum value C of the average value of the environmental parameter of item a in the historical equipment use environment records of the medium voltage cabinet a,max , minimum value C a,min ; Evaluate the equipment reference degree of the plurality of historical medium voltage cabinets to the medium voltage cabinet, wherein the specific process of evaluating the equipment reference degree of the d-th historical medium voltage cabinet to the medium voltage cabinet is as follows: Obtain the characteristic values ​​of various environmental parameters in the d-th historical medium voltage cabinet, and calculate the reference degree E of the marking device of the d-th historical medium voltage cabinet to the medium voltage cabinet d : Wherein, m represents the total number of the environmental parameters of the medium voltage cabinet; B x B is represented by the characteristic value of the xth environmental parameter in the medium voltage cabinet; d x It is represented as the characteristic value of the x-th environmental parameter in the d-th historical medium voltage cabinet; When the marking device reference degree E d If the value is greater than a preset marking device reference degree threshold, it is determined that the d-th historical medium voltage cabinet has equipment reference value for 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.

4. The intelligent host management system applicable to medium voltage cabinets according to claim 2 is characterized in that: The characteristic parameter action data module includes a characteristic parameter action data unit; The characteristic parameter action data unit is used to obtain each reference historical medium voltage cabinet of the medium voltage cabinet, obtain the historical operation records of the reference historical medium voltage cabinet, evaluate the degree of action between various equipment operating parameters in the parameter history medium voltage cabinet, and obtain the characteristic parameter action data of the parameter history medium voltage cabinet.

5. The intelligent host management system applicable to medium voltage cabinets according to claim 4 is characterized in that: The characteristic parameter action data unit includes: Acquire each reference historical medium voltage cabinet of the medium voltage cabinet, acquire the historical operation record of the reference historical medium voltage cabinet, and acquire the historical operation data from the historical operation record, wherein the historical operation data includes data corresponding to the operation parameters of each device in the reference historical medium voltage cabinet; The degree of influence between the equipment operating parameters in the parameter history medium voltage cabinet is evaluated, wherein the specific process of evaluating the degree of influence of the f-th equipment operating parameter in the parameter history medium voltage cabinet on the g-th equipment operating parameter is as follows: Obtain the mean value of the f-th equipment operation parameter in each historical operation record of the parameter history medium voltage cabinet, and construct a parameter action regression model y of the f-th equipment operation parameter in the parameter history medium voltage cabinet and the g-th equipment operation parameter g : y g =U0+U f ·x f +ε, Among them, U0 represents the intercept; U f It is represented as the regression coefficient of the f-th equipment operating parameter in the parameter history medium voltage cabinet; ε is the error term; Based on the preset division ratio, each historical operation record in the parameter history medium voltage cabinet is divided into a training set and a test set, and the parameter action regression model y is calculated using the training set. g The characteristic regression error S g : Wherein, β is the total number of historical equipment operation records of the parameter history medium voltage cabinet in the training set; y g,q is the actual value of the mean value of the g-th equipment operation parameter in the q-th historical equipment operation record of the parameter history medium voltage cabinet in the training set; y` g,q is the parameter effect regression model y g a predicted value of the mean value of the g-th equipment operation parameter in the q-th historical equipment operation record; The feature regression error S g Minimize and obtain the parameter effect regression model y g The design matrix X is: Among them, x f ,1,x f ,2,...,x f,β are respectively represented as the mean values ​​of the f-th equipment operation parameters in the 1st, 2nd, ..., βth historical equipment operation records of the parameter history medium voltage cabinet in the training set; Get the parameter effect regression model y g The data vector Y in: Among them, y g,1 ,y g,2 ,...,y g,β are respectively represented as the mean values ​​of the g-th equipment operation parameter in the 1st, 2nd, ..., βth historical equipment operation records of the parameter history medium voltage cabinet in the training set; Get the vector U of regression coefficients in the parameter action regression model yg: U=(X T ·X) -1 ·X T ·Y, Wherein, U = (U0, U f ) ; Using the test set, calculate the parameter effect regression model y g The root mean square error R g : Wherein, ζ represents the total number of historical equipment operation records of the parameter history medium voltage cabinet in the test set; y △ g,z is the actual value of the mean value of the g-th equipment operation parameter in the z-th historical equipment operation record of the parameter history medium voltage cabinet in the test set; △、 g,z is the parameter effect regression model y g a predicted value of the mean value of the g-th equipment operation parameter in the z-th historical equipment operation record; When the root mean square error R g If the error is less than the preset error threshold, it is determined that the parameter effect regression model yg is constructed, and the parameter effect regression model y is obtained. g The regression coefficient U of the fth equipment operating parameter is f And standardize it; Get the parameter effect regression model y g The regression coefficient U f , when the regression coefficient U f The absolute value of is greater than the preset characteristic regression threshold value U′, determining the effect degree of the f-th equipment operation parameter in the parameter history medium voltage cabinet on the g-th equipment operation parameter, and recording the f-th equipment operation parameter as the effect equipment operation parameter of the g-th equipment operation parameter; The effect equipment operating parameters of several equipment operating parameters in the parameter history medium voltage cabinet are obtained and collected to obtain the characteristic parameter effect data of the parameter history medium voltage cabinet.

6. The intelligent host management system applicable to medium voltage cabinets according to claim 4 is characterized in that: The target action data module includes a characteristic action value unit and a target action data unit; The characteristic action value unit is used to obtain the characteristic parameter action data of each reference history medium voltage cabinet of the medium voltage cabinet, and calculate the characteristic action value between the various equipment operation parameters in the medium voltage cabinet; The target action data unit is used to analyze the action between the equipment operating parameters in the medium voltage cabinet according to the characteristic action value to obtain the target action data of the medium voltage cabinet.

7. The intelligent host management system applicable to medium voltage cabinets according to claim 6 is characterized in that: The characteristic action value unit comprises: Acquire characteristic parameter action data of each reference history medium voltage cabinet of the medium voltage cabinet, and acquire action operation parameters corresponding to the equipment operation parameters in the reference history medium voltage cabinet from the characteristic parameter action data; Calculate the characteristic action value between the equipment operating parameters in the medium voltage cabinet, where the characteristic action value K of the h-th equipment operating parameter in the medium voltage cabinet on the v-th equipment operating parameter h,v The calculation process is: Obtain the hth item of equipment operating parameters in the characteristic parameter action data, which is a number of reference historical medium voltage cabinets of the equipment operating parameters of the vth item of equipment operating parameters, and the characteristic action value K h,v The specific calculation formula is: Wherein, δ represents the total number of reference history medium voltage cabinets of the medium voltage cabinet, and δ is greater than a preset feature quantity threshold; U h v,α It is represented by the regression coefficient of the vth equipment operating parameter in the parameter effect regression model of the hth equipment operating parameter in the αth reference historical medium voltage cabinet of the medium voltage cabinet on the vth equipment operating parameter.

8. The intelligent host management system applicable to medium voltage cabinets according to claim 6 is characterized in that: The target action data unit includes: When the characteristic action value K h,v is greater than a preset first characteristic action threshold value K1, it is determined that the h-th equipment operation parameter in the medium voltage cabinet has a positive effect on the v-th equipment operation parameter, and the h-th equipment operation parameter is recorded as the positive effect equipment operation parameter of the v-th equipment operation parameter; When the characteristic action value K h,v is less than the preset second characteristic action threshold value K2, it is determined that the h-th equipment operation parameter in the medium voltage cabinet has a negative effect on the v-th equipment operation parameter, and the h-th equipment operation parameter is recorded as the negative effect equipment operation parameter of the v-th equipment operation parameter, wherein K1>0>K2; The equipment operating parameters of the medium voltage cabinet having positive effect equipment operating parameters and negative effect equipment operating parameters are acquired and collected to obtain target effect data of the medium voltage cabinet.

9. The intelligent host management system applicable to medium voltage cabinets according to claim 6 is characterized in that: The model optimization module includes a model optimization unit; The model optimization unit is used 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 several equipment operation parameters of the medium voltage cabinet having positive action equipment operation parameters and negative action equipment operation parameters, obtain characteristic action values ​​between the several equipment operation parameters and the positive action equipment operation parameters, or the 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 record of the medium voltage cabinet.

10. The intelligent host management system applicable to medium voltage cabinets according to claim 9, characterized in that: The smart host management module includes an intelligent host management unit; The intelligent host management unit is used to monitor the equipment operation status of the medium voltage cabinet in the current cycle using the smart host, and input the data monitored by the smart host into the fault detection model. The fault detection model generates equipment evaluation data of the medium voltage cabinet. The equipment evaluation data includes the equipment failure probability of the medium voltage cabinet and data corresponding to various equipment operation parameters. The equipment evaluation data is uploaded to the user's mobile communication device through the smart host. The smart host performs intelligent device management of the medium voltage cabinet according to the instructions of the user on the mobile communication device.

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