Power transmission and transformation equipment data analysis system and method under heterogeneous transmission model

Abstract

The invention discloses a power transmission and transformation equipment data analysis system and method under a heterogeneous transmission model, and relates to the technical field of equipment data analysis, and the method comprises the steps: evaluating the equipment abnormality degree of power transmission and transformation equipment in a historical monitoring record; acquiring abnormal time data of the power transmission and transformation equipment based on the abnormal historical monitoring record, acquiring historical power consumption records of optical cable equipment for performing power distribution on the power transmission and transformation equipment based on the abnormal time data, and analyzing power consumption relevance between different optical cable equipment to obtain associated equipment data; acquiring a historical power consumption record and a historical equipment operation record of the optical cable equipment, and constructing a power consumption prediction model of the optical cable equipment in combination with the associated equipment data of the optical cable equipment; and monitoring the equipment state of the optical cable equipment, predicting the power consumption of the optical cable equipment by using the power consumption prediction model to obtain predicted power consumption data, and adjusting the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment data analysis, and in particular to a system and method for analyzing power transmission and transformation equipment data under a heterogeneous transmission model. Background Art

[0002] The heterogeneous transmission model is a model for data transmission and interaction between different types of systems, platforms or databases. The heterogeneous transmission model is used to analyze the data of the power transmission and transformation equipment that supplies power to the optical cable equipment in the optical cable, including but not limited to the following benefits: 1. Improving the accuracy of data analysis. The use of heterogeneous transmission models can effectively manage the data characteristics of different types of equipment and fully understand the operating status of the equipment, thereby improving the accuracy of data analysis; 2. Efficient integration of multi-source data. The operation of power transmission and transformation equipment involves multiple data sources. The heterogeneous transmission model can efficiently integrate multiple data, making the analyzed data more comprehensive; 3. The heterogeneous transmission model can perform real-time data analysis at different levels, providing faster response time to deal with emergencies.

[0003] In order to save costs and make full use of resources in real life, most optical cables and optical cable equipment will be installed in cable tunnels for data transmission. Optical cable equipment needs to adjust its working status according to different scenarios and needs. Optical cable equipment in different working states consumes different amounts of electricity, and different optical cable equipment needs to work together to ensure data communication of the optical cable. However, the current power transmission and transformation equipment cannot intelligently adjust the power supply to the optical cable equipment according to actual conditions, which may not only waste electricity, but may also cause problems with data transmission of the optical cable due to insufficient power supply to the optical cable equipment, affecting data communication. Summary of the invention

[0004] The object of the present invention is to provide a data analysis system and method for power transmission and transformation equipment under a heterogeneous transmission model to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model, the method comprising: Step S100: Obtain historical monitoring records of power transmission and transformation equipment, evaluate the degree of equipment abnormality of the power transmission and transformation equipment in the historical monitoring records, and obtain abnormal historical monitoring records; Step S200: based on the abnormal historical monitoring records, the abnormal time data of the power transmission and transformation equipment is obtained, based on the abnormal time data, the historical power consumption records of the optical cable equipment for power distribution of the power transmission and transformation equipment are obtained, the power consumption correlation between different optical cable equipment is analyzed, and the associated equipment data is obtained; Step S300: Obtain historical power consumption records and historical equipment operation records of the optical cable equipment, and build a power consumption prediction model for the optical cable equipment in combination with associated equipment data of the optical cable equipment; Step S400: monitor the equipment status of the optical cable equipment, and use the power consumption prediction model to predict the power consumption of the optical cable equipment to obtain predicted power consumption data, and adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

[0006] Furthermore, step S100 includes: Step S101: Acquire various historical monitoring records of power transmission and transformation equipment, and extract historical monitoring data from the historical monitoring records, wherein the historical monitoring data includes average values ​​of various monitoring indicators of the power transmission and transformation equipment; Obtaining S102: Calculating characteristic discrete values ​​β of monitoring indicators in power transmission and transformation equipment; Step S103: Acquire each characteristic data range of the monitoring index in the power transmission and transformation equipment, wherein the cth characteristic data range D of the monitoring index c =[μ-c×β,μ+c×β]; Step S104: Calculate the feature proportion E of the cth feature data range c =B c,sum / B sum , where B c,sum is the average value of the monitoring index, the total number of historical monitoring records within the cth characteristic data range, B sum The total number of historical monitoring records of power transmission and transformation equipment; Step S105: Obtain the characteristic proportion E of the cth characteristic data range of the monitoring indicator c-1 , Get the preset feature ratio threshold E', when E c >E´ and E c-1 ≤E´, the cth feature data range D c , recorded as the target data range of the monitoring indicator; Step S106: obtaining target data ranges of various monitoring indicators in the power transmission and transformation equipment, evaluating the degree of equipment abnormality of the power transmission and transformation equipment in various historical monitoring records, and obtaining abnormal historical monitoring records of the power transmission and transformation equipment.

[0007] Furthermore, in step S102, the characteristic discrete value β of the monitoring index in the power transmission and transformation equipment is calculated, and the specific formula is: Calculate the characteristic discrete value β of the monitoring index in the power transmission and transformation equipment: , Where m is the total number of historical monitoring records; A iis the average value of the monitoring indicators in the i-th historical monitoring record of the power transmission and transformation equipment; μ is the mean of the average values ​​of the monitoring indicators in each historical monitoring record.

[0008] Furthermore, in step S106, the abnormality degree of the power transmission and transformation equipment in each historical monitoring record is evaluated, and the specific process is as follows: Evaluate the abnormality of the power transmission and transformation equipment in each historical monitoring record, wherein the abnormality of the power transmission and transformation equipment in the fth historical monitoring record is evaluated. The specific process is as follows: When the average value of a certain monitoring indicator in the f-th historical monitoring record of the power transmission and transformation equipment is out of the corresponding target data range, it is determined that the power transmission and transformation equipment in the f-th historical monitoring record is abnormal, and the f-th historical monitoring record is recorded as an abnormal historical monitoring record of the power transmission and transformation equipment.

[0009] Further, step S200 includes: Step S201: Obtain abnormal historical monitoring records of power transmission and transformation equipment, obtain the historical period to which the abnormal historical monitoring records belong, record the historical period as the abnormal historical period of the power transmission and transformation equipment, and aggregate the various abnormal historical periods of the power transmission and transformation equipment to obtain abnormal time data of the power transmission and transformation equipment; Step S202: Obtain each optical cable device that is distributed by the power transmission and transformation equipment, obtain the historical power consumption record of the optical cable device in the abnormal historical period, obtain the total power consumption of the optical cable device from the historical power consumption record, and analyze the power consumption correlation between each optical cable device, wherein the power consumption correlation between the g-th optical cable device and the h-th optical cable device is analyzed. The specific process is as follows: Calculate the power consumption correlation value R between the gth optical cable device and the hth optical cable device g,h ; Step S203: Setting a first power consumption associated threshold R' 1 , the second power consumption associated threshold R´ 2 , where R´ 1 >0>R´ 2 , when R g,h >R´ 1 , determine that the power consumption of the g-th optical cable device and the h-th optical cable device has a positive correlation, and record the g-th optical cable device as the associated optical cable device of the h-th optical cable device; When R g,h <R´ 2 , determine that the power consumption of the g-th optical cable device and the h-th optical cable device has a negative correlation, and record the g-th optical cable device as the associated optical cable device of the h-th optical cable device; When R´ 2 ≤R g,h ≤R´1 , it is determined that the power consumption of the g-th optical cable device and the h-th optical cable device is not correlated; Step S204: Collecting the associated optical cable devices of the plurality of optical cable devices that distribute power to the power transmission and transformation equipment to obtain the associated device data of the power transmission and transformation equipment; The power consumption correlation analysis between the optical cable devices in the power transmission and transformation equipment in the above steps is because in order to ensure the normal data communication of the optical cable, mutual assistance between different optical cable devices is required. By obtaining the power consumption correlation between the optical cable devices, the prediction accuracy of the power consumption prediction model constructed subsequently can be improved.

[0010] Furthermore, in step S202, the power consumption correlation value R between the g-th optical cable device and the h-th optical cable device is calculated. g,h , the specific formula is: Calculate the power consumption correlation value R between the gth optical cable device and the hth optical cable device g,h : , Where n is the total number of historical power consumption records of the g-th optical cable equipment; K g,z is the total power consumption of the g-th optical cable device in the z-th historical power consumption record; K h,z It is the total power consumption of the hth optical cable device in the zth historical power consumption record.

[0011] Furthermore, step S300 includes: Step S301: obtaining historical equipment operation records of each optical cable equipment of the power transmission and transformation equipment, and obtaining the average value of each equipment operation parameter of the optical cable equipment from the equipment operation records; Step S302: Acquire the associated equipment data of each optical cable equipment, and construct a power consumption prediction model for each optical cable equipment, wherein the power consumption prediction model for the uth optical cable equipment of the power transmission and transformation equipment is constructed. The specific process is as follows: Obtain a training set and a test set of the u-th optical cable device, and obtain associated optical cable devices of the u-th optical cable device, wherein the α-th data group of the training set includes the historical power consumption record of the u-th optical cable device in the α-th historical period, and the historical device operation record of the u-th optical cable device and the corresponding associated optical cable device in the α-1-th historical period; The equipment operating parameters of the u-th optical cable equipment and the corresponding associated optical cable equipment are used as the input data of the model, and the total power consumption of the u-th optical cable equipment is used as the target output data of the model; Use the training set to train the power consumption prediction model and obtain the power consumption prediction model Q of the u-th optical cable device. u ; Step S303: Obtain power consumption prediction model Qu The feature activation function S(x): , Get the power consumption prediction model Q u The preset weight matrix ζ and bias γ in the calculation result are used to obtain the power consumption prediction model Q u The output τ of the input layer in which the power consumption prediction model Q u The output V of the hidden layer in is: , Among them, ζ´ is the weight matrix in the hidden layer; γ´ is the bias in the hidden layer; Get the power consumption prediction model Q u The output ξ of the output layer in is: , Among them, △ is the weight matrix in the output layer; γ △ is the bias in the output layer; Using the test set, calculate the power consumption prediction model Q u The characteristic model error L; Step S304: updating the weight matrix and bias of the power consumption prediction model until the characteristic model error L is less than a preset characteristic error threshold, and determining that the power consumption prediction model of the u-th optical cable device is completed.

[0012] Furthermore, in step S303, the power consumption prediction model Q is calculated. u The characteristic model error L is as follows: Calculate the characteristic model error L of the power consumption prediction model: , Where p is the total number of training samples in the training set; y w is the total power consumption of the uth optical cable device in the i-th data group of the training set; y' w is the predicted value of the total power consumption of the u-th optical cable device in the i-th data group in the training set.

[0013] Furthermore, step S400 includes: Step S401: monitoring the equipment status of each optical cable equipment that distributes power to the power transmission and transformation equipment in the current cycle, and obtaining the average value of each equipment operating parameter of each optical cable equipment in the current cycle; Step S402: Obtain the associated device data of each optical cable device, and use the power consumption prediction model corresponding to each optical cable device to predict the total power consumption of each optical cable device in the next cycle, obtain the predicted power consumption data of each optical cable device, and adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

[0014] In order to better implement the above method, a data analysis system for power transmission and transformation equipment under a heterogeneous transmission model is also proposed, the system includes an abnormality assessment module, a correlation analysis module, a prediction model establishment module, and an intelligent power distribution module; The abnormality assessment module is used to assess the abnormality degree of the power transmission and transformation equipment in the historical monitoring records to obtain the abnormal historical monitoring records; The correlation analysis module is used to obtain the historical power consumption records of the optical cable equipment used for power distribution of the power transmission and transformation equipment, analyze the power consumption correlation between different optical cable equipment, and obtain the associated equipment data; A prediction model building module is used to build a power consumption prediction model for optical cable equipment; The intelligent power distribution module is used to adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

[0015] Further, the anomaly assessment module includes a target data range unit and an anomaly assessment unit; The target data range unit is used to obtain the target data range of each monitoring indicator in the power transmission and transformation equipment; The abnormality assessment unit is used to obtain abnormal historical monitoring records based on the target data range and the equipment abnormality degree of the power transmission and transformation equipment in each historical monitoring record.

[0016] Further, the correlation analysis module includes a power consumption correlation value unit and a correlation analysis unit; A power consumption correlation value unit is used to calculate the power consumption correlation value between various optical cable devices in the power transmission and transformation equipment; The correlation analysis unit is used to analyze the power consumption correlation between various optical cable devices according to the power consumption correlation value to obtain the correlation device data.

[0017] Furthermore, the prediction model building module includes a data acquisition unit and a model optimization unit; A data acquisition unit, used to acquire historical equipment operation records of each optical cable equipment of the power transmission and transformation equipment; The model optimization unit is used to construct a power consumption prediction model for each optical cable device based on the associated device data and historical device operation records of each optical cable device.

[0018] Further, the intelligent power distribution module includes an intelligent power distribution unit; The intelligent power distribution unit is used to use the power consumption prediction model corresponding to the optical cable equipment to predict the total power consumption of the optical cable equipment in the next cycle and to intelligently adjust the power distribution strategy of the power transmission and transformation equipment.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes the intelligent adjustment of the power distribution strategy of the power transmission and transformation equipment, ensures the normal operation of the optical cable equipment distributed by the power transmission and transformation equipment, obtains the historical cycle of abnormalities through abnormal analysis of the historical monitoring records of the abnormalities of the power transmission and transformation equipment, and analyzes the power consumption correlation between different optical cable equipment, and understands the degree of mutual influence between the optical cable equipment. Finally, a power consumption prediction model for the optical cable equipment is established, so that the power transmission and transformation equipment can intelligently adjust the power supply to the optical cable equipment according to the actual situation, which not only reduces the waste of power resources, but also ensures the stability of the power supply of the optical cable equipment, and greatly reduces the probability of problems in the data transmission of the optical cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a method flow chart of a method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model of the present invention; Figure 2 It is a module schematic diagram of a power transmission and transformation equipment data analysis system under a heterogeneous transmission model of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model, the method comprising: Step S100: Obtain historical monitoring records of power transmission and transformation equipment, evaluate the degree of equipment abnormality of the power transmission and transformation equipment in the historical monitoring records, and obtain abnormal historical monitoring records; Wherein, step S100 includes: Step S101: acquiring various historical monitoring records of power transmission and transformation equipment, and extracting historical monitoring data from the historical monitoring records, wherein the historical monitoring data includes average values ​​of various monitoring indicators of the power transmission and transformation equipment; For example, various monitoring indicators include voltage and power of power transmission and transformation equipment; Obtaining S102: Calculating characteristic discrete values ​​β of monitoring indicators in power transmission and transformation equipment; Step S103: Acquire each characteristic data range of the monitoring index in the power transmission and transformation equipment, wherein the cth characteristic data range D of the monitoring index c =[μ-c×β,μ+c×β]; Step S104: Calculate the feature proportion E of the cth feature data range c =B c,sum / B sum , where B c,sum is the average value of the monitoring index, the total number of historical monitoring records within the cth characteristic data range, B sum The total number of historical monitoring records of power transmission and transformation equipment; Step S105: Obtain the characteristic proportion E of the cth characteristic data range of the monitoring indicator c-1 , Get the preset feature ratio threshold E', when E c >E´ and E c-1 ≤E´, the cth feature data range D c , recorded as the target data range of the monitoring indicator; Step S106: obtaining target data ranges of various monitoring indicators in the power transmission and transformation equipment, evaluating the degree of equipment abnormality of the power transmission and transformation equipment in various historical monitoring records, and obtaining abnormal historical monitoring records of the power transmission and transformation equipment; Among them, in step S102, the characteristic discrete value β of the monitoring indicator in the power transmission and transformation equipment is calculated, and the specific formula is: Calculate the characteristic discrete value β of the monitoring index in the power transmission and transformation equipment: , Where m is the total number of historical monitoring records; A i is the average value of the monitoring indicators in the i-th historical monitoring record of the power transmission and transformation equipment; μ is the mean of the average values ​​of the monitoring indicators in each historical monitoring record; For example, m is 3; A 1 is 4; A 2 is 6; A 3 is 8; μ is 6; Calculate the characteristic discrete value β of the monitoring index in the power transmission and transformation equipment: , Among them, in step S106, the abnormality degree of the power transmission and transformation equipment in each historical monitoring record is evaluated, and the specific process is as follows: Evaluate the abnormality of the power transmission and transformation equipment in each historical monitoring record, wherein the abnormality of the power transmission and transformation equipment in the fth historical monitoring record is evaluated. The specific process is as follows: When the average value of a certain monitoring indicator in the f-th historical monitoring record of the power transmission and transformation equipment is not within the corresponding target data range, it is determined that the power transmission and transformation equipment in the f-th historical monitoring record is abnormal, and the f-th historical monitoring record is recorded as an abnormal historical monitoring record of the power transmission and transformation equipment; Step S200: based on the abnormal historical monitoring records, the abnormal time data of the power transmission and transformation equipment is obtained, based on the abnormal time data, the historical power consumption records of the optical cable equipment for power distribution of the power transmission and transformation equipment are obtained, the power consumption correlation between different optical cable equipment is analyzed, and the associated equipment data is obtained; Wherein, step S200 includes: Step S201: Obtain abnormal historical monitoring records of power transmission and transformation equipment, obtain the historical period to which the abnormal historical monitoring records belong, record the historical period as the abnormal historical period of the power transmission and transformation equipment, and aggregate the various abnormal historical periods of the power transmission and transformation equipment to obtain abnormal time data of the power transmission and transformation equipment; Step S202: Obtain each optical cable device that is distributed by the power transmission and transformation equipment, obtain the historical power consumption record of the optical cable device in the abnormal historical period, obtain the total power consumption of the optical cable device from the historical power consumption record, and analyze the power consumption correlation between each optical cable device, wherein the power consumption correlation between the g-th optical cable device and the h-th optical cable device is analyzed. The specific process is as follows: Calculate the power consumption correlation value R between the gth optical cable device and the hth optical cable device g,h ; Step S203: Setting a first power consumption associated threshold R' 1 , the second power consumption associated threshold R´ 2 , where R´ 1 >0>R´ 2 , when R g,h >R´ 1 , determine that the power consumption of the g-th optical cable device and the h-th optical cable device has a positive correlation, and record the g-th optical cable device as the associated optical cable device of the h-th optical cable device; When R g,h <R´ 2 , determine that the power consumption of the g-th optical cable device and the h-th optical cable device has a negative correlation, and record the g-th optical cable device as the associated optical cable device of the h-th optical cable device; When R´ 2 ≤R g,h ≤R´ 1 , it is determined that the power consumption of the g-th optical cable device and the h-th optical cable device is not correlated; Step S204: Collecting the associated optical cable devices of the plurality of optical cable devices that distribute power to the power transmission and transformation equipment to obtain the associated device data of the power transmission and transformation equipment; In step S202, the power consumption correlation value R between the g-th optical cable device and the h-th optical cable device is calculated. g,h , the specific formula is: Calculate the power consumption correlation value R between the gth optical cable device and the hth optical cable device g,h : , Where n is the total number of historical power consumption records of the g-th optical cable equipment; K g,z is the total power consumption of the g-th optical cable device in the z-th historical power consumption record; K h,z is the total power consumption of the hth optical cable device in the zth historical power consumption record; Step S300: Obtain historical power consumption records and historical equipment operation records of the optical cable equipment, and build a power consumption prediction model for the optical cable equipment in combination with associated equipment data of the optical cable equipment; Wherein, step S300 includes: Step S301: obtaining historical equipment operation records of each optical cable equipment of the power transmission and transformation equipment, and obtaining the average value of each equipment operation parameter of the optical cable equipment from the equipment operation records; For example, various equipment operating parameters, including voltage, current, and power of optical cable equipment; Step S302: Acquire the associated equipment data of each optical cable equipment, and construct a power consumption prediction model for each optical cable equipment, wherein the power consumption prediction model for the uth optical cable equipment of the power transmission and transformation equipment is constructed. The specific process is as follows: Obtain a training set and a test set of the u-th optical cable device, and obtain associated optical cable devices of the u-th optical cable device, wherein the α-th data group of the training set includes the historical power consumption record of the u-th optical cable device in the α-th historical period, and the historical device operation record of the u-th optical cable device and the corresponding associated optical cable device in the α-1-th historical period; The equipment operating parameters of the u-th optical cable equipment and the corresponding associated optical cable equipment are used as the input data of the model, and the total power consumption of the u-th optical cable equipment is used as the target output data of the model; Use the training set to train the power consumption prediction model and obtain the power consumption prediction model Q of the u-th optical cable device. u ; Step S303: Obtain power consumption prediction model Q u The feature activation function S(x): , Get the power consumption prediction model Q u The preset weight matrix ζ and bias γ in the calculation result are used to obtain the power consumption prediction model Q u The output τ of the input layer in which the power consumption prediction model Q u The output V of the hidden layer in is: , Among them, ζ´ is the weight matrix in the hidden layer; γ´ is the bias in the hidden layer; Get the power consumption prediction model Qu The output ξ of the output layer in is: , Among them, △ is the weight matrix in the output layer; γ △ is the bias in the output layer; Using the test set, calculate the power consumption prediction model Q u The characteristic model error L; Step S304: updating the weight matrix and bias of the power consumption prediction model until the characteristic model error L is less than a preset characteristic error threshold, and determining that the power consumption prediction model of the u-th optical cable device is completed; In step S303, the power consumption prediction model Q is calculated. u The characteristic model error L is as follows: Calculate the characteristic model error L of the power consumption prediction model: , Where p is the total number of training samples in the training set; y w is the total power consumption of the uth optical cable device in the i-th data group of the training set; y' w is the predicted value of the total power consumption of the u-th optical cable device in the i-th data group of the training set; Step S400: monitoring the equipment status of the optical cable equipment, and using the power consumption prediction model to predict the power consumption of the optical cable equipment to obtain predicted power consumption data, and adjusting the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data; Wherein, step S400 includes: Step S401: monitoring the equipment status of each optical cable equipment that distributes power to the power transmission and transformation equipment in the current cycle, and obtaining the average value of each equipment operating parameter of each optical cable equipment in the current cycle; Step S402: Acquire the associated device data of each optical cable device, and use the power consumption prediction model corresponding to each optical cable device to predict the total power consumption of each optical cable device in the next cycle, obtain the predicted power consumption data of each optical cable device, and adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data; In order to better implement the above method, a data analysis system for power transmission and transformation equipment under a heterogeneous transmission model is also proposed, the system includes an abnormality assessment module, a correlation analysis module, a prediction model establishment module, and an intelligent power distribution module; The abnormality assessment module is used to assess the abnormality degree of the power transmission and transformation equipment in the historical monitoring records to obtain the abnormal historical monitoring records; The correlation analysis module is used to obtain the historical power consumption records of the optical cable equipment used for power distribution of the power transmission and transformation equipment, analyze the power consumption correlation between different optical cable equipment, and obtain the associated equipment data; A prediction model building module is used to build a power consumption prediction model for optical cable equipment; Intelligent power distribution module, used to adjust the power distribution strategy of power transmission and transformation equipment according to the predicted power consumption data; Among them, the anomaly assessment module includes a target data range unit and an anomaly assessment unit; The target data range unit is used to obtain the target data range of each monitoring indicator in the power transmission and transformation equipment; The abnormality evaluation unit is used to evaluate the abnormality degree of the power transmission and transformation equipment in each historical monitoring record according to the target data range, and obtain the abnormal historical monitoring record; Wherein, the correlation analysis module includes a power consumption correlation value unit and a correlation analysis unit; A power consumption correlation value unit is used to calculate the power consumption correlation value between various optical cable devices in the power transmission and transformation equipment; A correlation analysis unit, used to analyze the power consumption correlation between various optical cable devices according to the power consumption correlation value, and obtain the correlation device data; Among them, the prediction model building module includes a data acquisition unit and a model optimization unit; A data acquisition unit, used to acquire historical equipment operation records of each optical cable equipment of the power transmission and transformation equipment; A model optimization unit is used to construct a power consumption prediction model for each optical cable device based on the associated device data and historical device operation records of each optical cable device; Wherein, the intelligent power distribution module includes an intelligent power distribution unit; The intelligent power distribution unit is used to use the power consumption prediction model corresponding to the optical cable equipment to predict the total power consumption of the optical cable equipment in the next cycle and to intelligently adjust the power distribution strategy of the power transmission and transformation equipment.

[0023] 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. A data analysis method for power transmission and transformation equipment under a heterogeneous transmission model, characterized in that: The method comprises: Step S100: Acquire historical monitoring records of power transmission and transformation equipment, evaluate the degree of equipment abnormality of the power transmission and transformation equipment in the historical monitoring records, and obtain abnormal historical monitoring records; Step S200: based on the abnormal historical monitoring record, obtaining abnormal time data of the power transmission and transformation equipment, based on the abnormal time data, obtaining historical power consumption records of the optical cable equipment that distributes power to the power transmission and transformation equipment, analyzing the power consumption correlation between different optical cable equipment, and obtaining associated equipment data; Step S300: Acquire historical power consumption records and historical equipment operation records of the optical cable equipment, and construct a power consumption prediction model for the optical cable equipment in combination with associated equipment data of the optical cable equipment; Step S400: monitor the equipment status of the optical cable equipment, and use the power consumption prediction model to predict the power consumption of the optical cable equipment to obtain predicted power consumption data, and adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

2. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 1 is characterized in that: The step S100 includes: Step S101: acquiring various historical monitoring records of the power transmission and transformation equipment, and extracting historical monitoring data from the historical monitoring records, wherein the historical monitoring data includes average values ​​of various monitoring indicators of the power transmission and transformation equipment; Acquisition S102: Calculating a characteristic discrete value β of a monitoring indicator in the power transmission and transformation equipment; Step S103: Acquire each characteristic data range of the monitoring indicator in the power transmission and transformation equipment, wherein the cth characteristic data range D of the monitoring indicator c =[μ-c×β,μ+c×β]; Step S104: Calculate the feature proportion E of the c-th feature data range c =B c,sum / B sum , where B c,sum is the average value of the monitoring index, the total number of historical monitoring records within the cth characteristic data range, B sum The total number of historical monitoring records of the power transmission and transformation equipment; Step S105: Obtain the characteristic proportion E of the cth characteristic data range of the monitoring indicator c-1 , Get the preset feature ratio threshold E', when E c >E´ and E c-1 ≤E´, the cth feature data range D c , recorded as the target data range of the monitoring indicator; Step S106: obtaining the target data range of each monitoring indicator in the power transmission and transformation equipment, evaluating the equipment abnormality degree of the power transmission and transformation equipment in each historical monitoring record, and obtaining the abnormal historical monitoring record of the power transmission and transformation equipment.

3. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 2 is characterized in that: In step S102, the characteristic discrete value β of the monitoring indicator in the power transmission and transformation equipment is calculated, and the specific formula is: Calculate the characteristic discrete value β of the monitoring index in the power transmission and transformation equipment: ， Wherein, m is the total number of the historical monitoring records; A i is the average value of the monitoring index in the i-th historical monitoring record of the power transmission and transformation equipment; μ is the mean of the average values ​​of the monitoring index in each historical monitoring record.

4. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 3 is characterized in that: In step S106, the abnormality degree of the power transmission and transformation equipment in each of the historical monitoring records is evaluated, and the specific process is as follows: Evaluate the degree of abnormality of the power transmission and transformation equipment in each of the historical monitoring records, wherein the degree of abnormality of the power transmission and transformation equipment in the fth historical monitoring record is evaluated in the following specific process: When the average value of a certain monitoring indicator in the f-th historical monitoring record of the power transmission and transformation equipment is outside the corresponding target data range, it is determined that the power transmission and transformation equipment in the f-th historical monitoring record is abnormal, and the f-th historical monitoring record is recorded as an abnormal historical monitoring record of the power transmission and transformation equipment.

5. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 2 is characterized in that: The step S200 includes: Step S201: Obtain the abnormal historical monitoring record of the power transmission and transformation equipment, obtain the historical period to which the abnormal historical monitoring record belongs, record the historical period as the abnormal historical period of the power transmission and transformation equipment, and aggregate the various abnormal historical periods of the power transmission and transformation equipment to obtain abnormal time data of the power transmission and transformation equipment; Step S202: Obtain each optical cable device that is distributed by the power transmission and transformation equipment, obtain the historical power consumption record of the optical cable device in the abnormal historical period, obtain the total power consumption of the optical cable device from the historical power consumption record, and analyze the power consumption correlation between the optical cable devices, wherein the power consumption correlation between the g-th optical cable device and the h-th optical cable device is analyzed. The specific process is as follows: Calculate the power consumption correlation value R between the g-th optical cable device and the h-th optical cable device g,h ; Step S203: Setting a first power consumption associated threshold R'1 and a second power consumption associated threshold R'2, wherein R'1>0>R'2, when R g,h >R´1, determine that the power consumption of the g-th optical cable device is positively correlated with the h-th optical cable device, and record the g-th optical cable device as the associated optical cable device of the h-th optical cable device; When R g,h <R´2, it is determined that there is a negative correlation between the power consumption of the g-th optical cable device and the h-th optical cable device, and the g-th optical cable device is recorded as the associated optical cable device of the h-th optical cable device; When R´2≤R g,h ≤R´1, it is determined that the power consumption of the g-th optical cable device and the h-th optical cable device is not correlated; Step S204: Collecting the associated optical cable devices of the plurality of optical cable devices that distribute power to the power transmission and transformation equipment to obtain the associated device data of the power transmission and transformation equipment.

6. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 5 is characterized in that: In step S202, the power consumption correlation value R between the g-th optical cable device and the h-th optical cable device is calculated. g,h , the specific formula is: Calculate the power consumption correlation value R between the g-th optical cable device and the h-th optical cable device g,h : ， Wherein, n is the total number of historical power consumption records of the g-th optical cable device; K g,z K is the total power consumption of the g-th optical cable device in the z-th historical power consumption record; h,z is the total power consumption of the hth optical cable device in the zth historical power consumption record.

7. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 5, characterized in that: The step S300 includes: Step S301: obtaining historical equipment operation records of each optical cable equipment of the power transmission and transformation equipment, and obtaining average values ​​of various equipment operation parameters of the optical cable equipment from the equipment operation records; Step S302: Acquire the associated device data of each optical cable device, and construct a power consumption prediction model for each optical cable device, wherein the power consumption prediction model for the uth optical cable device of the power transmission and transformation device is constructed, and the specific process is as follows: Obtaining a training set and a test set of the u-th optical cable device, and obtaining an associated optical cable device of the u-th optical cable device, wherein the α-th data group of the training set includes a historical power consumption record of the u-th optical cable device in an α-th historical period, and a historical device operation record of the u-th optical cable device and the corresponding associated optical cable device in an α-1-th historical period; Taking the various equipment operating parameters of the u-th optical cable equipment and the corresponding associated optical cable equipment as the input data of the model, and taking the total power consumption of the u-th optical cable equipment as the target output data of the model; Using the training set, the power consumption prediction model is trained to obtain the power consumption prediction model Q of the u-th optical cable device. u ; Step S303: Obtain the power consumption prediction model Q u The feature activation function S(x): ， Obtain the power consumption prediction model Q u The weight matrix ζ and bias γ preset in the above example are used to obtain the power consumption prediction model Q u The output of the input layer τ, where the power consumption prediction model Q u The output V of the hidden layer in is: ， Wherein, ζ´ is the weight matrix in the hidden layer; γ´ is the bias in the hidden layer; Obtain the power consumption prediction model Q u The output ξ of the output layer in is: ， Among them, △ is the weight matrix in the output layer; γ △ is the bias in the output layer; Using the test set, calculate the power consumption prediction model Q u The characteristic model error L; Step S304: updating the weight matrix and bias of the power consumption prediction model until the characteristic model error L is less than a preset characteristic error threshold, and determining that the power consumption prediction model of the u-th optical cable device is completed.

8. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 7 is characterized in that: In step S303, the power consumption prediction model Q is calculated. u The characteristic model error L is as follows: Calculate the characteristic model error L of the power consumption prediction model: ， Where p is the total number of training samples in the training set; y w is the total power consumption of the u-th optical cable device in the i-th data group of the training set; y' w is the predicted value of the total power consumption of the u-th optical cable device in the i-th data group of the training set.

9. The method for analyzing data of power transmission and transformation equipment under a heterogeneous transmission model according to claim 7, characterized in that: The step S400 includes: Step S401: monitoring the equipment status of each optical cable equipment that distributes power to the power transmission and transformation equipment in the current cycle, and obtaining the average value of each equipment operating parameter of each optical cable equipment in the current cycle; Step S402: Obtain the associated device data of each optical cable device, and use the power consumption prediction model corresponding to each optical cable device to predict the total power consumption of each optical cable device in the next cycle, so as to obtain the predicted power consumption data of each optical cable device, and adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

10. A data analysis system for power transmission and transformation equipment under a heterogeneous transmission model, used to execute a data analysis method for power transmission and transformation equipment under a heterogeneous transmission model as claimed in any one of claims 1 to 9, characterized in that: The system includes an abnormality assessment module, a correlation analysis module, a prediction model building module, and an intelligent power distribution module; The abnormality assessment module is used to assess the degree of abnormality of the power transmission and transformation equipment in the historical monitoring record to obtain the abnormal historical monitoring record; The correlation analysis module is used to obtain the historical power consumption records of the optical cable equipment that distributes power to the power transmission and transformation equipment, analyze the power consumption correlation between different optical cable equipment, and obtain the associated equipment data; The prediction model building module is used to build a power consumption prediction model for the optical cable equipment; The intelligent power distribution module is used to adjust the power distribution strategy of the power transmission and transformation equipment according to the predicted power consumption data.

11. The data analysis system for power transmission and transformation equipment under a heterogeneous transmission model according to claim 10, characterized in that: The anomaly assessment module includes a target data range unit and an anomaly assessment unit; The target data range unit is used to obtain the target data range of each monitoring indicator in the power transmission and transformation equipment; The abnormality assessment unit is used to obtain abnormal historical monitoring records based on the target data range and the equipment abnormality degree of the power transmission and transformation equipment in each historical monitoring record.

12. The data analysis system for power transmission and transformation equipment under a heterogeneous transmission model according to claim 10, characterized in that: The association analysis module includes a power consumption association value unit and an association analysis unit; The power consumption correlation value unit is used to calculate the power consumption correlation value between each optical cable device in the power transmission and transformation equipment; The correlation analysis unit is used to analyze the power consumption correlation between the optical cable devices according to the power consumption correlation value to obtain the correlation device data.

13. The data analysis system for power transmission and transformation equipment under a heterogeneous transmission model according to claim 10, characterized in that: The prediction model building module includes a data acquisition unit and a model optimization unit; The data acquisition unit is used to acquire the historical equipment operation records of each optical cable equipment of the power transmission and transformation equipment; The model optimization unit is used to construct a power consumption prediction model for each optical cable device based on the associated device data and historical device operation records of each optical cable device.

14. The data analysis system for power transmission and transformation equipment under a heterogeneous transmission model according to claim 10, characterized in that: The intelligent power distribution module includes an intelligent power distribution unit; The intelligent power distribution unit is used to use the power consumption prediction model corresponding to the optical cable equipment to predict the total power consumption of the optical cable equipment in the next cycle and to intelligently adjust the power distribution strategy of the power transmission and transformation equipment.

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