Cable equipment intelligent power supply system and method based on big data

Abstract

The invention discloses a cable equipment intelligent power supply system and method based on big data, and relates to the technical field of equipment intelligent power supply, and the method comprises the steps: obtaining a historical equipment operation record and a historical power consumption record of cable equipment, and analyzing the power consumption state of the cable equipment during operation; analyzing the monitoring indexes in the historical monitoring records and the influence degree of the monitoring indexes on the power consumption state of the cable equipment to obtain characteristic power consumption influence data; acquiring a historical power consumption record and a historical equipment operation record of the cable equipment, constructing a power consumption prediction model of the cable equipment in combination with the historical monitoring record of the cable equipment, and optimizing the power consumption prediction model based on the characteristic power consumption influence data of the cable equipment to obtain a target power consumption prediction model; and performing power consumption prediction on the cable equipment by using the target power consumption prediction model in the current period, adjusting a power supply strategy of the cable equipment, and performing intelligent power supply on the cable equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power supply for equipment, and specifically to an intelligent power supply system and method for cable equipment based on big data. Background Art

[0002] At present, with the construction and development of infrastructure such as urban underground railways, various municipal pipe network facilities such as urban underground communication, electricity, gas, water supply and drainage have gradually become centralized to form a unified underground integrated pipe gallery. Therefore, there are more and more types of cables in the underground cable track, including power cables, fiber optic cables, control cables, etc. Some types of cables in the underground cable track need to use cable equipment to process the cables. For example, fiber optic cables need to use cable equipment to achieve optoelectronic conversion for transmission. Some cable equipment requires batteries to supply power to the cable equipment during operation. This requires the cable equipment to reasonably utilize electric energy and supply power according to the actual needs of the equipment. However, at present, most cable equipment cannot dynamically adjust the power supply according to the actual needs, which will not only greatly reduce the equipment time, but even cause equipment damage and huge losses. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent power supply system and method for cable equipment based on big data to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An intelligent power supply method for cable equipment based on big data, the method includes: Step S100: Obtain the historical equipment operation records and historical power consumption records of the cable equipment, analyze the power consumption status of the cable equipment during operation, and obtain the target historical power consumption records; Step S200: Obtain the characteristic time data of the target historical power consumption records of the cable equipment, based on the characteristic time data, obtain the historical monitoring records of the cable equipment, analyze the monitoring indicators in the historical monitoring records, and obtain the characteristic power consumption impact data on the power consumption status of the cable equipment; Step S300: Obtain the historical power consumption records and historical equipment operation records of the cable equipment, and combine the historical monitoring records of the cable equipment to construct a power consumption prediction model for the cable equipment. Based on the characteristic power consumption impact data of the cable equipment, optimize the power consumption prediction model to obtain the target power consumption prediction model; Step S400: Use the target power consumption prediction model in the current cycle to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment.

[0005] Further, step S100 includes: Step S101: Obtain the historical device operation records of the cable device. Based on the historical device operation records, obtain the historical time period of the device operation of the cable device, and record it as the marked historical time period of the cable device. Step S102: Set the unit time period T´, obtain each historical power consumption record of the cable device, and obtain the historical power consumption data from the historical power consumption records. The historical power consumption data includes the total power consumption of the cable device in the historical power consumption records, where the duration of the historical time period is one unit time period. Step S103: Obtain the total number M of each historical power consumption record. Sort each historical power consumption record in chronological order from front to back, and calculate the characteristic power consumption ratio value of each historical power consumption record of the cable device. Among them, the characteristic power consumption ratio value B of the a-th historical power consumption record of the cable device a : , where H a represents the total power consumption of the cable device in the a-th historical power consumption record; H sum represents the total power of the device battery of the cable device. Step S104: When the characteristic power consumption ratio value B a is greater than the preset characteristic power consumption ratio threshold, it is determined that the cable device is in a high power consumption state within the a-th historical power consumption record, and the a-th historical power consumption record is recorded as the target power consumption record. In the above steps, the historical power consumption records are screened because in the actual process, the cable device consumes power during standby and also consumes power during operation, and the power consumption conditions of the device in different operating states are also different. Analyze the power consumption state of the cable device during operation to obtain the target historical power consumption record, so as to provide an effective data source for analyzing the influence degree of the monitoring indicators in the historical monitoring records on the power consumption state of the cable device in the following text, and further ensure the rationality and scientificity of the power supply to the cable device.

[0006] Furthermore, step S200 includes: Step S201: Obtain the target historical power consumption record of the cable device, and obtain the characteristic time data of the target historical power consumption record. The characteristic time data is the historical time period to which the target historical power consumption record belongs. Step S202: Obtain the historical time period to which the historical target historical power consumption record belongs in the cable device, and record it as the characteristic historical time period of the cable device. Use the monitoring device to monitor the cable device within the characteristic historical time period to obtain the historical monitoring record of the cable device. Step S203: Obtain each historical monitoring record of the cable equipment, and obtain historical monitoring data from the historical monitoring records. The historical monitoring data includes data corresponding to various monitoring indicators of the cable equipment, and obtain the average value of each monitoring indicator from the historical monitoring records; For example, the various monitoring indicators include environmental temperature, environmental humidity, etc.; Step S204: Analyze the influence degree of each monitoring indicator of the cable equipment on the power consumption status of the cable equipment. Among them, the specific process of analyzing the influence degree of the c-th monitoring indicator of the cable equipment on the power consumption status of the cable equipment is as follows: Calculate the characteristic influence degree value D of the c-th monitoring indicator on the power consumption status of the cable equipment c : , where n represents the total number of characteristic historical periods of the cable equipment; F i represents the total power consumption of the cable equipment in the i-th characteristic historical period; E c,i represents the average value of the c-th monitoring indicator of the cable equipment in the i-th characteristic historical period; Step S205: Set the first characteristic influence degree value D´ 1 , the second characteristic influence degree value D´ 2 , where D´ 1 >0>D´ 2 , when D c >D´ 1 , it is determined that the c-th monitoring indicator has a positive influence on the power consumption of the cable equipment, and the c-th monitoring indicator is recorded as the positive influence monitoring indicator of the cable equipment. When D c <D´ 2 , it is determined that the c-th monitoring indicator has a negative influence on the power consumption of the cable equipment, and the c-th monitoring indicator is recorded as the negative influence monitoring indicator of the cable equipment; Step S206: Obtain the characteristic influence degree values of each positive influence monitoring indicator and each negative influence monitoring indicator of the cable equipment and gather them to obtain the characteristic power consumption influence data of the cable equipment.

[0007] Further, step S300 includes: Step S301: Obtain the historical power consumption record of the cable equipment, obtain the historical equipment operation record of the cable equipment, and combine the historical monitoring record of the cable equipment to construct a power consumption prediction model for the cable equipment. Among them, the specific process of constructing the power consumption prediction model is as follows: Extract historical equipment operation data from the historical equipment operation record. The historical equipment operation data includes data corresponding to various equipment operation parameters of the cable equipment; For example, the operating parameters of each device include device voltage, device current, etc.; Step S302: Use the total power consumption of the cable device in the historical power consumption records as the output data of the power consumption prediction model, use the average value of each device operating parameter in the cable device in the historical device operation records as the input data of the power consumption prediction model, and use the average value of each monitoring index of the cable device in the historical monitoring records as the input data of the power consumption prediction model; Step S303: Aggregate the various historical power consumption records, various historical device operation records, and various historical device operation records of the cable device to obtain the data set of the power consumption prediction model. Among them, the historical power consumption records, historical device operation records, and historical device operation records in the same historical period are used as a data group in the data set, and based on a preset ratio, the data set is divided into a training set and a test set; Step S304: Use the training set to train the power consumption prediction model, and use the test set to test the power consumption prediction model, and calculate the characteristic model determination coefficient R of the power consumption prediction model: , where z represents the total number of data groups in the test set; y z represents the total power consumption of the cable device in the historical power consumption record in the z-th data group of the test set; y △ z represents the average value of the total power consumption of the cable device in the historical power consumption record in each data group of the test set; y´ z represents the predicted value of the total power consumption of the cable device in the z-th data group on the power consumption prediction model; Step S305: When the characteristic model determination coefficient R is greater than the preset characteristic model determination coefficient, it is determined that the power consumption prediction model has model performance, and the power consumption prediction model is denoted as the target power consumption prediction model of the cable device; Step S306: When the characteristic model determination coefficient R is less than or equal to the preset characteristic model determination coefficient, it is determined that the power consumption prediction model does not have model performance. Obtain the characteristic power consumption impact data of the cable device, extract the positive impact monitoring indicators of the cable device and the characteristic impact degree values of the negative impact monitoring indicators from the characteristic power consumption impact data, optimize the power consumption prediction model, and adjust the various model parameters of the power consumption prediction model until the characteristic model determination coefficient R is greater than the characteristic model determination coefficient, and denote the power consumption prediction model as the target power consumption prediction model of the cable device.

[0008] Further, step S400 includes: Step S401: Set the characteristic unit duration. At intervals of one characteristic unit duration within the current cycle, obtain the monitoring data and equipment operation data of the cable equipment for various equipment operation parameters and monitoring indicators of the cable equipment. Step S402: Obtain the average value of various equipment operation parameters of the cable equipment from the monitoring data, and obtain the average value of various equipment operation parameters of the cable equipment from the equipment operation data. Use the target power consumption prediction model of the cable equipment to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment.

[0009] To better implement the above method, an intelligent power supply system for cable equipment based on big data is also proposed. The system includes a power consumption status analysis module, a power consumption impact analysis module, a model optimization module, and an intelligent power supply module. The power consumption status analysis module is used to analyze the power consumption status of the cable equipment during operation to obtain the target historical power consumption records. The power consumption impact analysis module is used to analyze the monitoring indicators in the historical monitoring records and obtain the characteristic power consumption impact data on the power consumption status of the cable equipment. The model optimization module is used to construct the power consumption prediction model of the cable equipment, and optimize the power consumption prediction model based on the characteristic power consumption impact data of the cable equipment to obtain the target power consumption prediction model. The intelligent power supply module is used to use the target power consumption prediction model to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment.

[0010] Furthermore, the power consumption status analysis module includes a characteristic power consumption ratio value unit and a power consumption status analysis unit. The characteristic power consumption ratio value unit is used to calculate the characteristic power consumption ratio values of each historical power consumption record of the cable equipment. The power consumption status analysis unit is used to analyze the power consumption status of the cable equipment during operation based on the characteristic power consumption ratio values to obtain the target power consumption records.

[0011] Furthermore, the power consumption impact analysis module includes a characteristic impact degree value unit and a power consumption impact analysis unit. The characteristic impact degree value unit is used to calculate the characteristic impact degree values of various monitoring indicators of the cable equipment on the power consumption status of the cable equipment. The power consumption impact analysis unit is used to analyze the impact degree of various monitoring indicators of the cable equipment on the power consumption status of the cable equipment based on the characteristic impact degree values to obtain the characteristic power consumption impact data.

[0012] Furthermore, the model optimization module includes a model construction unit and a model optimization unit. A model construction unit is configured to obtain the historical power consumption records of cable equipment, acquire the historical equipment operation records of the cable equipment, and construct a power consumption prediction model for the cable equipment in combination with the historical monitoring records of the cable equipment. A model optimization unit is configured to optimize the power consumption prediction model according to the characteristic power consumption impact data of the cable equipment, adjust various model parameters of the power consumption prediction model, and obtain a target power consumption prediction model.

[0013] Furthermore, the intelligent power supply module includes an intelligent power supply unit. The intelligent power supply unit is configured to use the target power consumption prediction model of the cable equipment to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply to the cable equipment.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention realizes the intelligent power supply of cable equipment. By analyzing the power consumption of cable equipment in different historical periods, the historical power consumption records with high power consumption are obtained and recorded as target historical power consumption records. Through the target historical power consumption records, the influence of monitoring indicators on the equipment power consumption of the cable equipment is analyzed to obtain characteristic power consumption impact data. Subsequently, the characteristic power consumption impact data is used to optimize the power consumption prediction model of the cable equipment, and the target power consumption prediction model is used to predict the power consumption of the cable equipment. According to the prediction results, the power supply strategy of the cable equipment is adjusted, thereby greatly reducing the power consumption of the cable equipment during daily use and significantly increasing the service life of the cable equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a method flow chart of an intelligent power supply system and method for cable equipment based on big data according to the present invention; Figure 2 is a module schematic diagram of an intelligent power supply system and method for cable equipment based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Hereinafter, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, an intelligent power supply method for cable equipment based on big data, and the method includes: Step S100: Obtain the historical equipment operation records and historical power consumption records of the cable equipment, analyze the power consumption status of the cable equipment during operation, and obtain the target historical power consumption records; Among them, step S100 includes: Step S101: Obtain the historical equipment operation records of the cable equipment. Based on the historical equipment operation records, obtain the historical time period of the equipment operation of the cable equipment, and record it as the marked historical time period of the cable equipment; Step S102: Set the unit time period T´, obtain each historical power consumption record of the cable equipment, and obtain the historical power consumption data from the historical power consumption records. The historical power consumption data includes the total power consumption of the cable equipment in the historical power consumption records. Among them, the duration of the historical time period is one unit time period; Step S103: Obtain the total number M of each historical power consumption record. Sort each historical power consumption record in chronological order from front to back, and calculate the characteristic power consumption ratio value of each historical power consumption record of the cable equipment. Among them, the characteristic power consumption ratio value B of the a-th historical power consumption record of the cable equipment a : , Among them, H a represents the total power consumption of the cable equipment in the a-th historical power consumption record; H sum represents the total power of the equipment battery of the cable equipment; For example, the total number M of each historical power consumption record of the cable equipment is expressed as 10; the unit time period T´ is expressed as one hour; the total power consumption H of the cable equipment in the 2nd historical power consumption record 2 is expressed as 20W; the total power H of the equipment battery of the cable equipment sum is expressed as 100W; Calculate the characteristic power consumption ratio value B of the 2nd historical power consumption record of the cable equipment 2 : , Step S104: When the characteristic power consumption ratio value B a is greater than the preset characteristic power consumption ratio threshold, it is determined that the cable equipment is in a high power consumption state within the a-th historical power consumption record, and the a-th historical power consumption record is recorded as the target power consumption record; Step S200: Obtain the characteristic time data of the target historical power consumption record of the cable equipment. Based on the characteristic time data, obtain the historical monitoring records of the cable equipment, analyze the monitoring indicators in the historical monitoring records, and obtain the characteristic power consumption impact data on the power consumption status of the cable equipment; Among them, step S200 includes: Step S201: Obtain the target historical power consumption records of the cable equipment, and obtain the characteristic time data of the target historical power consumption records. The characteristic time data is the historical period to which the target historical power consumption records belong. Step S202: Obtain the historical period to which the historical target historical power consumption records in the cable equipment belong, denoted as the characteristic historical period of the cable equipment. Use the monitoring equipment to monitor the cable equipment within the characteristic historical period to obtain the historical monitoring records of the cable equipment. Step S203: Obtain each historical monitoring record of the cable equipment, and obtain the historical monitoring data from the historical monitoring records. The historical monitoring data includes the data corresponding to each monitoring index of the cable equipment, and obtain the average value of each monitoring index from the historical monitoring records. Step S204: Analyze the influence degree of each monitoring index of the cable equipment on the power consumption state of the cable equipment. Among them, the specific process of analyzing the influence degree of the c-th monitoring index of the cable equipment on the power consumption state of the cable equipment is as follows: Calculate the characteristic influence degree value D of the c-th monitoring index on the power consumption state of the cable equipment c : , where n represents the total number of characteristic historical periods of the cable equipment; F i represents the total power consumption of the cable equipment in the i-th characteristic historical period; E c,i represents the average value of the c-th monitoring index of the cable equipment in the i-th characteristic historical period. Step S205: Set the first characteristic influence degree value D´ 1 , the second characteristic influence degree value D´ 2 , where D´ 1 > 0 > D´ 2 , when D c > D´ 1 , it is determined that the c-th monitoring index has a positive influence on the power consumption of the cable equipment, and the c-th monitoring index is recorded as the positive influence monitoring index of the cable equipment. When D c < D´ 2 , it is determined that the c-th monitoring index has a negative influence on the power consumption of the cable equipment, and the c-th monitoring index is recorded as the negative influence monitoring index of the cable equipment. Step S206: Obtain the characteristic influence degree values of each positive influence monitoring index and each negative influence monitoring index of the cable equipment and gather them to obtain the characteristic power consumption influence data of the cable equipment. Step S300: Obtain the historical power consumption records and historical device operation records of the cable device, and combine the historical monitoring records of the cable device to construct a power consumption prediction model for the cable device. Based on the characteristic power consumption impact data of the cable device, optimize the power consumption prediction model to obtain the target power consumption prediction model; Among them, step S300 includes: Step S301: Obtain the historical power consumption records of the cable device, obtain the historical device operation records of the cable device, and combine the historical monitoring records of the cable device to construct a power consumption prediction model for the cable device. Among them, the specific process of constructing the power consumption prediction model is as follows: Extract the historical device operation data from the historical device operation records. The historical device operation data includes the data corresponding to the various device operation parameters of the cable device; Step S302: Take the total power consumption of the cable device in the historical power consumption records as the output data of the power consumption prediction model, take the average value of the various device operation parameters in the cable device in the historical device operation records as the input data of the power consumption prediction model, and take the average value of the various monitoring indicators of the cable device in the historical monitoring records as the input data of the power consumption prediction model; Step S303: Collect the various historical power consumption records, various historical device operation records, and various historical device operation records of the cable device to obtain the data set of the power consumption prediction model. Among them, take the historical power consumption records, historical device operation records, and historical device operation records within the same historical period as a data group in the data set, and divide the data set into a training set and a test set based on a preset ratio; Step S304: Use the training set to train the power consumption prediction model, and use the test set to test the power consumption prediction model, and calculate the characteristic model determination coefficient R of the power consumption prediction model: , Among them, z represents the total number of data groups in the test set; y z represents the total power consumption of the cable device in the z-th data group of the test set in the historical power consumption records; y △ z represents the average value of the total power consumption of the cable device in the historical power consumption records in each data group of the test set; y' z represents the predicted value of the total power consumption of the cable device in the z-th data group on the power consumption prediction model; Step S305: When the characteristic model determination coefficient R is greater than the preset characteristic model determination coefficient, determine that the power consumption prediction model has model performance, and record the power consumption prediction model as the target power consumption prediction model of the cable device; Step S306: When the determination coefficient R of the feature model is less than or equal to the preset determination coefficient of the feature model, it is determined that the power consumption prediction model does not have model performance. Obtain the characteristic power consumption impact data of the cable equipment. From the characteristic power consumption impact data, extract the characteristic impact degree values of the positive impact monitoring indicators and the negative impact monitoring indicators of each item of the cable equipment, optimize the power consumption prediction model, and adjust the various model parameters of the power consumption prediction model until the determination coefficient R of the feature model is greater than the determination coefficient of the feature model. Denote the power consumption prediction model as the target power consumption prediction model of the cable equipment; Step S400: Use the target power consumption prediction model to predict the power consumption of the cable equipment in the current cycle, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment; Among them, step S400 includes: Step S401: Set the characteristic unit time length. Every other characteristic unit time length in the current cycle, obtain the monitoring data and equipment operation data of the cable equipment for each equipment operation parameter and each monitoring indicator of the cable equipment; Step S402: Obtain the average value of each equipment operation parameter of the cable equipment from the monitoring data, obtain the average value of each equipment operation parameter of the cable equipment from the equipment operation data, use the target power consumption prediction model of the cable equipment to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment; In order to better implement the above method, an intelligent power supply system for cable equipment based on big data is also proposed. The system includes a power consumption status analysis module, a power consumption impact analysis module, a model optimization module, and an intelligent power supply module; The power consumption status analysis module is used to analyze the power consumption status of the cable equipment during operation to obtain the target historical power consumption records; The power consumption impact analysis module is used to analyze the monitoring indicators in the historical monitoring records and the degree of influence on the power consumption status of the cable equipment to obtain the characteristic power consumption impact data; The model optimization module is used to construct the power consumption prediction model of the cable equipment, and optimize the power consumption prediction model based on the characteristic power consumption impact data of the cable equipment to obtain the target power consumption prediction model; The intelligent power supply module is used to use the target power consumption prediction model to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment; Among them, the power consumption status analysis module includes a characteristic power consumption ratio value unit and a power consumption status analysis unit; The characteristic power consumption ratio value unit is used to calculate the characteristic power consumption ratio values of each historical power consumption record of the cable equipment; The power consumption status analysis unit is used to analyze the power consumption status of the cable equipment during operation according to the characteristic power consumption ratio value, and obtain the target power consumption record; Among them, the power consumption impact analysis module includes a characteristic impact degree value unit and a power consumption impact analysis unit; The characteristic impact degree value unit is used to calculate the monitoring indicators of the cable equipment and the characteristic impact degree value of the cable equipment on the power consumption status; The power consumption impact analysis unit is used to analyze the impact degree of the monitoring indicators of the cable equipment on the power consumption status of the cable equipment according to the characteristic impact degree value, and obtain the characteristic power consumption impact data; Among them, the model optimization module includes a model construction unit and a model optimization unit; The model construction unit is used to obtain the historical power consumption record of the cable equipment, obtain the historical equipment operation record of the cable equipment, and combine the historical monitoring record of the cable equipment to construct the power consumption prediction model of the cable equipment; The model optimization unit is used to optimize the power consumption prediction model according to the characteristic power consumption impact data of the cable equipment, adjust the model parameters of the power consumption prediction model, and obtain the target power consumption prediction model; Among them, the intelligent power supply module includes an intelligent power supply unit; The intelligent power supply unit is used to use the target power consumption prediction model of the cable equipment to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply on the cable equipment.

[0018] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for intelligent power supply of cable equipment based on big data, characterized in that: The method comprises: Step S100: acquiring historical equipment operation records and historical power consumption records of the cable equipment, analyzing the power consumption state of the cable equipment during operation, and obtaining target historical power consumption records; Step S200: Acquire characteristic time data of the target historical power consumption record of the cable device, acquire the historical monitoring record of the cable device based on the characteristic time data, analyze the monitoring indicators in the historical monitoring record, and the degree of influence on the power consumption state of the cable device to obtain characteristic power consumption influence data; Step S300: Obtain the historical power consumption records and historical equipment operation records of the cable equipment, and build a power consumption prediction model for the cable equipment in combination with the historical monitoring records of the cable equipment, and optimize the power consumption prediction model based on the characteristic power consumption impact data of the cable equipment to obtain a target power consumption prediction model; Step S400: using the target power consumption prediction model in the current cycle to predict the power consumption of the cable equipment, and adjusting the power supply strategy of the cable equipment to intelligently power the cable equipment.

2. According to the big data-based intelligent power supply method for cable equipment according to claim 1, it is characterized in that: The step S100 includes: Step S101: acquiring historical equipment operation records of a cable equipment, acquiring historical equipment operation periods of the cable equipment based on the historical equipment operation records, and recording them as marked historical periods of the cable equipment; Step S102: setting a unit time length T´, obtaining each historical power consumption record of the cable device, and obtaining historical power consumption data from the historical power consumption records, wherein the historical power consumption data includes the total power consumption of the cable device in the historical power consumption records, wherein the duration of the historical period is a unit time length; Step S103: Obtain the total number M of the historical power consumption records, sort the historical power consumption records from front to back in chronological order, and calculate the characteristic power consumption ratio value of the historical power consumption records of the cable device, wherein the characteristic power consumption ratio value B of the a-th historical power consumption record of the cable device is a : ， Among them, H a It is represented by the total power consumption of the cable equipment in the ath historical power consumption record; H sum The total battery charge of the device representing the cable device; Step S104: When the characteristic power consumption ratio value B a If the power consumption ratio is greater than a preset characteristic power consumption ratio threshold, it is determined that the cable device in the a-th historical power consumption record is in a high power consumption state, and the a-th historical power consumption record is recorded as a target power consumption record.

3. According to the big data-based intelligent power supply method for cable equipment in claim 1, it is characterized in that: The step S200 includes: Step S201: Acquire the target historical power consumption record of the cable device, and acquire characteristic time data of the target historical power consumption record, wherein the characteristic time data is the historical time period to which the target historical power consumption record belongs; Step S202: obtaining the historical period to which the historical target historical power consumption record belongs in the cable device, recording it as the characteristic historical period of the cable device, using a monitoring device to monitor the cable device within the characteristic historical period, and obtaining the historical monitoring record of the cable device; Step S203: acquiring each historical monitoring record of the cable equipment, acquiring historical monitoring data from the historical monitoring records, wherein the historical monitoring data includes data corresponding to each monitoring indicator of the cable equipment, and acquiring an average value of each monitoring indicator from the historical monitoring records; Step S204: analyzing the influence of various monitoring indicators of the cable equipment on the power consumption state of the cable equipment, wherein the specific process of analyzing the influence of the cth monitoring indicator of the cable equipment on the power consumption state of the cable equipment is as follows: Calculate the characteristic impact value D of the monitoring index item c on the power consumption state of the cable equipment c : ， Wherein, n represents the total number of characteristic historical periods of the cable equipment; F i It is expressed as the total power consumption of the cable equipment in the i-th characteristic historical period; E c,i It is expressed as the average value of the cth monitoring indicator of the cable device in the i-th characteristic historical period; Step S205: Set the first feature influence degree value D´1 and the second feature influence degree value D´2, where D´1 > 0 > D´2. When D c > D´1, it is determined that the c-th monitoring index has a positive influence on the power consumption of the cable device, and the c-th monitoring index is recorded as the positive influence monitoring index of the cable device. When D c < D´2, it is determined that the c-th monitoring index has a negative influence on the power consumption of the cable device, and the c-th monitoring index is recorded as the negative influence monitoring index of the cable device; Step S206: Acquire various positive impact monitoring indicators of the cable equipment, and characteristic impact degree values ​​of various negative impact monitoring indicators, and aggregate them to obtain characteristic power consumption impact data of the cable equipment.

4. The method for intelligent power supply of cable equipment based on big data according to claim 3 is characterized in that: The step S300 includes: Step S301: Obtain the historical power consumption records of the cable equipment, obtain the historical equipment operation records of the cable equipment, and build a power consumption prediction model for the cable equipment in combination with the historical monitoring records of the cable equipment. The specific process of building the power consumption prediction model is as follows: Extracting historical equipment operation data from the historical equipment operation records, the historical equipment operation data including data corresponding to various equipment operation parameters of the cable equipment; Step S302: using the total power consumption of the cable equipment in the historical power consumption record as output data of the power consumption prediction model, using the average value of each equipment operation parameter in the cable equipment in the historical equipment operation record as input data of the power consumption prediction model, and using the average value of each monitoring index of the cable equipment in the historical monitoring record as input data of the power consumption prediction model; Step S303: Collecting various historical power consumption records, various historical equipment operation records and various historical equipment operation records of the cable equipment to obtain a data set of the power consumption prediction model, wherein the historical power consumption records, historical equipment operation records and historical equipment operation records in the same historical period are taken as a data group in the data set, and the data set is divided into a training set and a test set based on a preset ratio; Step S304: Use the training set to train the power consumption prediction model, and use the test set to test the power consumption prediction model, and calculate the characteristic model determination coefficient R of the power consumption prediction model: ， Wherein, z represents the total number of data groups in the test set; y z It is represented by the total power consumption of the cable equipment in the historical power consumption record in the zth data group of the test set; △ z It is represented by the average value of the total power consumption of the cable equipment in the historical power consumption record in each data group of the test set; z The predicted value of the total power consumption of the cable equipment in the z-th data group based on the power consumption prediction model; Step S305: when the characteristic model determination coefficient R is greater than the preset characteristic model determination coefficient, it is determined that the power consumption prediction model has model performance, and the power consumption prediction model is recorded as the target power consumption prediction model of the cable equipment; Step S306: When the characteristic model determination coefficient R is less than or equal to the preset characteristic model determination coefficient, it is determined that the power consumption prediction model does not have model performance, and the characteristic power consumption impact data of the cable equipment is obtained. From the characteristic power consumption impact data, the positive impact monitoring indicators of the cable equipment and the characteristic impact degree values ​​of the negative impact monitoring indicators are extracted, the power consumption prediction model is optimized, and the model parameters of the power consumption prediction model are adjusted until the characteristic model determination coefficient R is greater than the characteristic model determination coefficient. The power consumption prediction model is recorded as the target power consumption prediction model of the cable equipment.

5. The method for intelligent power supply of cable equipment based on big data according to claim 4, characterized in that: The step S400 includes: Step S401: setting a characteristic unit duration, and obtaining monitoring data and equipment operation data of the cable equipment by performing various equipment operation parameters and various monitoring indicators of the cable equipment at intervals of one characteristic unit duration in the current cycle; Step S402: Obtain the average values ​​of various equipment operating parameters of the cable equipment from the monitoring data, obtain the average values ​​of various equipment operating parameters of the cable equipment from the equipment operation data, use the target power consumption prediction model of the cable equipment to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply for the cable equipment.

6. A cable equipment intelligent power supply system based on big data, used to execute a cable equipment intelligent power supply method based on big data as described in any one of claims 1 to 5, characterized in that: The system includes a power consumption status analysis module, a power consumption impact analysis module, a model optimization module, and an intelligent power supply module; The power consumption status analysis module is used to analyze the power consumption status of the cable equipment during operation to obtain a target historical power consumption record; The power consumption impact analysis module is used to analyze the monitoring indicators in the historical monitoring records to determine the degree of influence on the power consumption state of the cable equipment, and obtain characteristic power consumption impact data; The model optimization module is used to construct a power consumption prediction model for the cable equipment, and optimize the power consumption prediction model based on the characteristic power consumption impact data of the cable equipment to obtain a target power consumption prediction model; The intelligent power supply module is used to use the target power consumption prediction model to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and perform intelligent power supply to the cable equipment.

7. The intelligent power supply system for cable equipment based on big data according to claim 6 is characterized in that: The power consumption status analysis module includes a characteristic power consumption ratio value unit and a power consumption status analysis unit; The characteristic power consumption ratio value unit is used to calculate the characteristic power consumption ratio value of each historical power consumption record of the cable device; The power consumption status analysis unit is used to analyze the power consumption status of the cable equipment during operation according to the characteristic power consumption ratio value to obtain a target power consumption record.

8. The intelligent power supply system for cable equipment based on big data according to claim 6 is characterized in that: The power consumption impact analysis module includes a characteristic impact degree value unit and a power consumption impact analysis unit; The characteristic impact degree value unit is used to calculate the characteristic impact degree value of each monitoring index of the cable equipment on the power consumption state of the cable equipment; The power consumption impact analysis unit is used to analyze the various monitoring indicators of the cable equipment according to the characteristic impact degree value, and the degree of influence on the power consumption state of the cable equipment to obtain characteristic power consumption impact data.

9. The intelligent power supply system for cable equipment based on big data according to claim 6 is characterized in that: The model optimization module includes a model building unit and a model optimization unit; The model building unit is used to obtain the historical power consumption records of the cable equipment, obtain the historical equipment operation records of the cable equipment, and build a power consumption prediction model for the cable equipment in combination with the historical monitoring records of the cable equipment; The model optimization unit is used to optimize the power consumption prediction model according to the characteristic power consumption impact data of the cable equipment, adjust various model parameters of the power consumption prediction model, and obtain a target power consumption prediction model.

10. The intelligent power supply system for cable equipment based on big data according to claim 6, characterized in that: The intelligent power supply module includes an intelligent power supply unit; The intelligent power supply unit is used to use the target power consumption prediction model of the cable equipment to predict the power consumption of the cable equipment, adjust the power supply strategy of the cable equipment, and intelligently power the cable equipment.

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