A power distribution module control system based on big data

Abstract

The present invention relates to the technical field of power-on modules, and discloses a power distribution module control system based on big data. The key points of the technical solution are that it includes a collection module, a processing module, a central controller and several different types of monitoring devices. The acquisition module includes several acquisition units, and the working status information of different types of monitoring devices is collected by each acquisition unit to obtain the collected data. The processing strategy compares the collected data with the benchmark comparison data to obtain the corresponding classification information and the corresponding processing value. The analysis strategy finds the corresponding classification information and execution value from the local database according to the classification information and the corresponding processing value, and executes the corresponding execution strategy, so that for the same type of monitoring equipment, if different working states are generated, different execution strategies will be corresponding, so that the processing result is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply modules, and more specifically to a power distribution module control system based on big data. Background Art

[0002] The power module control system consists of several monitoring modules and other auxiliary equipment. It is a monitoring system for data collection and processing of equipment and sensors in the base station, including equipment with sampling, data processing and data relay functions;

[0003] When the existing power-on module control system is working, it first collects the data monitored by different monitoring modules, and compares these data with the standard normal working state. When deviations occur, for devices under the same monitoring module, the power-on module control system often adopts the same processing method, resulting in a single processing method. Summary of the invention

[0004] In view of the deficiencies in the prior art, an object of the present invention is to provide a power distribution module control system based on big data, which is used to execute different processing methods for different collected data.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a power distribution module control system based on big data, comprising a collection module, a processing module, a central controller and several different types of monitoring devices, characterized in that: the collection module comprises several collection units, the collection units are used to collect working status information of different types of monitoring devices, obtain the collected data and send it to the processing module;

[0006] The processing module is provided with an information classification library, and the information classification library is used to store a plurality of classification information, each of which includes a plurality of benchmark comparison data and a corresponding processing value. The processing module is provided with a processing strategy, and when the processing strategy receives the collected data, the collected data and the benchmark comparison data are compared from the information classification library according to the collected data result to obtain the corresponding classification information and the corresponding processing value, and the classification information and the corresponding processing value are uploaded to the central controller;

[0007] The central controller is configured with an analysis strategy, the analysis strategy is used to analyze the classification information and the corresponding processing value, the central controller is configured with a local database, the local database stores execution information, the execution information includes the classification information and the corresponding execution value, and the execution strategy;

[0008] When the corresponding classification information exists in the local database, and in the classification information, the processing value is consistent with the execution value, the corresponding execution strategy is output, and the central controller sends the execution strategy to the execution device, and the execution device works in response to the execution strategy.

[0009] As a further improvement of the present invention, it also includes a local expert end and a cloud. When the corresponding classification information does not exist in the local database or the processing value under the classification information is inconsistent with the execution value, the classification information and the corresponding processing value are fed back to the local expert end, and at the same time uploaded to the cloud together with the processing strategy of the local expert end, storage data is generated and stored in the storage database, and the analysis strategy indexes the local database and the storage database at the same time.

[0010] As a further improvement of the present invention, the execution information includes a deviation range corresponding to each of the classification information. The analysis strategy compares the processing value with the execution value and outputs a first difference. The first difference represents the difference between the processing value and the execution value. When the processing value under the classification information is inconsistent with the execution value, if the first difference is within the deviation range, the corresponding execution strategy is output.

[0011] As a further improvement of the present invention, an expert processing time is preset in the analysis strategy. When the processing waiting time exceeds the expert processing time, the analysis strategy starts to analyze whether the first difference is within the deviation range.

[0012] As a further improvement of the present invention, the storage database includes abnormal data, the abnormal data includes abnormal strategies and corresponding classification information, and corresponding processing values. The cloud is provided with a warning module, the warning module includes a warning strategy, the warning module sends the abnormal data to the local expert end, the warning strategy analyzes the processing strategy of the local expert end and the abnormal strategy, when the abnormal strategy is consistent with the processing strategy of the local expert end, the warning strategy sends a warning signal to the local expert end to remind the local technical expert.

[0013] As a further improvement of the present invention, the exception strategy includes an exception step, the processing strategy of the local expert end includes a processing step, the exception strategy compares the exception step with the processing step being input by the local expert end, and marks the part in the processing step that is repeated with the exception step.

[0014] As a further improvement of the present invention, the monitoring device includes a temperature sensor, the collected data includes a temperature value, the temperature value represents the temperature collected by the temperature sensor in real time, the benchmark comparison data includes a first benchmark temperature value and a second benchmark temperature value, the first benchmark temperature value is greater than the second benchmark temperature value, when the temperature value is greater than the first benchmark temperature or the temperature value is less than the second benchmark temperature value, the execution strategy sends an alarm signal.

[0015] As a further improvement of the present invention, the benchmark comparison data includes a first temperature difference and a second temperature difference. When the difference between the temperature value and the first benchmark temperature value is greater than the first temperature difference or the difference between the temperature value and the second benchmark temperature value is greater than the second temperature difference, the execution strategy cuts off power.

[0016] As a further improvement of the present invention, the monitoring device includes an AC voltage sensor, the collected data includes an AC voltage value, the AC voltage value represents the AC voltage collected by the AC voltage sensor in real time, the benchmark comparison data includes a first reference voltage value and a second reference voltage value, the first reference voltage value is greater than the second reference voltage value, and when the AC voltage value is greater than the first reference voltage value or the AC voltage value is less than the second reference voltage value, the execution strategy sends an alarm signal.

[0017] As a further improvement of the present invention, the benchmark comparison data includes a first voltage difference and a second voltage difference. When the difference between the AC voltage value and the first benchmark voltage value is greater than the first voltage difference or the difference between the AC voltage value and the second benchmark voltage value is greater than the second voltage value, the execution strategy cuts off the power supply.

[0018] Beneficial effects of the present invention: The present invention collects working status information of different types of monitoring equipment through various collection units to obtain collected data. The processing strategy compares the collected data with the benchmark comparison data to obtain corresponding classification information and corresponding processing values. The analysis strategy finds the corresponding classification information and execution value from the local database according to the classification information and the corresponding processing value, and executes the corresponding execution strategy. For the same type of monitoring equipment, if different working states are generated, different execution strategies will be corresponding, so that the processing results are more perfect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural block diagram of the present invention;.

[0020] Figure numerals: 1. acquisition module; 11. acquisition unit; 2. processing module; 3. central controller; 4. monitoring device. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The same parts are represented by the same reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "bottom surface" and "top surface", "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0022] Reference Figure 1 As shown, a power distribution module control system based on big data in this embodiment includes a collection module 1, a processing module 2, a central controller 3 and a plurality of different types of monitoring devices 4, characterized in that: the collection module 1 includes a plurality of collection units 11, and the collection units 11 are used to collect working status information of different types of monitoring devices 4, obtain the collected data and send it to the processing module 2;

[0023] The processing module 2 is provided with an information classification library, and the information classification library is used to store a number of classification information. The classification information can be arranged and combined according to the working status information of different types of monitoring equipment that can be collected by the collection unit. According to the problems generated by the combination of multiple monitoring equipment, corresponding execution strategies are set to solve the corresponding problems, and processing values ​​are also provided. According to different degrees under the same condition, different processing values ​​correspond to corresponding execution strategies under different processing values, which improves processing efficiency and the accuracy of problem solving, so that the processing results are more complete. Each of the classification information includes a number of benchmark comparison data and corresponding processing values. The processing module 2 is configured with a processing strategy. When the processing strategy receives the collected data, according to the collection data result, the collected data and the benchmark comparison data are compared from the information classification library to obtain the corresponding classification information and the corresponding processing value, and the classification information and the corresponding processing value are uploaded to the central controller 3;

[0024] The central controller 3 is configured with an analysis strategy, the analysis strategy is used to analyze the classification information and the corresponding processing value, the central controller 3 is configured with a local database, the local database stores execution information, the execution information includes the classification information and the corresponding execution value, and the execution strategy;

[0025] When the corresponding classification information exists in the local database, and in the classification information, the processing value is consistent with the execution value, the corresponding execution strategy is output, and the central controller 3 sends the execution strategy to the execution device, and the execution device responds to the execution strategy.

[0026] It also includes a local expert end and a cloud end. The local expert end solves corresponding problems for technical experts and inputs the processing strategies of the technical experts on the device. The cloud end stores the processing strategies and results of the technical experts in solving the problems, and provides solutions for the next time this problem occurs, reducing the use of human resources and improving the efficiency of solving technical problems. When the corresponding classification information does not exist in the local database or the processing value under the classification information is inconsistent with the execution value, the classification information and the corresponding processing value are fed back to the local expert end, and uploaded to the cloud end together with the processing strategy of the local expert end, and storage data is generated and stored in the storage database. The analysis strategy indexes the local database and the storage database at the same time.

[0027] The execution information includes a deviation range corresponding to each of the classification information. The analysis strategy compares the processing value with the execution value and outputs a first difference. The first difference represents the difference between the processing value and the execution value. When the processing value under the classification information is inconsistent with the execution value, if the first difference is within the deviation range, the corresponding execution strategy is output. When the deviation between the processing value and the execution value is small, the execution strategy corresponding to the execution value can be directly executed to solve the technical problem. The analysis strategy is preset with an expert processing time. The expert processing time represents the estimated time when the expert starts to process the technical problem. The processing waiting time represents the time from the generation of the technical problem to the time when the technical expert starts to process the technical problem. When the processing waiting time exceeds the expert processing time, the analysis strategy starts to analyze whether the first difference is within the deviation range. When the processing value does not correspond to the execution value, the execution strategy corresponding to the execution value is executed, which provides a buffer to avoid direct execution and produce bad results.

[0028] Reference Figure 1As shown, the storage database includes abnormal data, and the abnormal data indicates that the technical expert adopts his own processing strategy, which causes problems or damage to the equipment. The abnormal data includes the abnormal strategy and the corresponding classification information, as well as the corresponding processing value. The cloud is provided with a warning module, and the warning module includes a warning strategy. The warning module sends the abnormal data to the local expert end. The warning strategy analyzes the processing strategy of the local expert end and the abnormal strategy. When the abnormal strategy is consistent with the processing strategy of the local expert end, the warning strategy sends a warning signal to the local expert end to remind the local technical expert to prevent the local technical expert from repeatedly adopting an erroneous processing strategy due to negligence or replacement of the local technical expert, resulting in equipment damage. The abnormal strategy includes an abnormal step, and the processing strategy of the local expert end includes a processing step. The abnormal strategy compares the abnormal step with the processing step being input by the local expert end, and marks the part of the processing step that repeats with the abnormal step, and marks the corresponding repeated part, so that the local technical expert can have a certain reference when executing his own processing strategy to avoid errors again.

[0029] The monitoring device 4 includes a temperature sensor, the collected data includes a temperature value, the temperature value represents the temperature collected by the temperature sensor in real time, the benchmark comparison data includes a first benchmark temperature value and a second benchmark temperature value, the first benchmark temperature value represents the high temperature that the device can withstand during operation, the second benchmark temperature value represents the low temperature that the device can withstand during operation, the first benchmark temperature value is greater than the second benchmark temperature value, when the temperature value is greater than the first benchmark temperature or the temperature value is less than the second benchmark temperature value, the execution strategy sends an alarm signal. The benchmark comparison data includes a first temperature difference value and a second temperature difference value, the first temperature difference represents the difference between the temperature at which the device may be damaged at high temperature and the first benchmark temperature value, the second temperature difference represents the difference between the temperature at which the device may be damaged at low temperature and the second benchmark temperature value, when the difference between the temperature value and the first benchmark temperature value is greater than the first temperature difference or the difference between the temperature value and the second benchmark temperature value is greater than the second temperature difference, the execution strategy cuts off the power supply.

[0030] The monitoring device 4 includes an AC voltage sensor, the collected data includes an AC voltage value, the AC voltage value represents the AC voltage collected by the AC voltage sensor in real time, the reference comparison data includes a first reference voltage value and a second reference voltage value, the first reference voltage value represents the voltage at which the device can work normally under high voltage conditions, and the second reference voltage value represents the voltage at which the device can work normally under mortgage conditions. The first reference voltage value is greater than the second reference voltage value, and when the AC voltage value is greater than the first reference voltage value or the AC voltage value is less than the second reference voltage value, the execution strategy sends an alarm signal. The reference comparison data includes a first voltage difference value and a second voltage difference value, the first voltage difference represents the difference between the voltage at which the device will be damaged under high voltage conditions and the first reference voltage value, and the second voltage difference represents the difference between the voltage at which the device will be damaged under low voltage conditions and the second reference voltage value, and when the difference between the AC voltage value and the first reference voltage value is greater than the first voltage difference or the difference between the AC voltage value and the second reference voltage value is greater than the second voltage value, the execution strategy cuts off the power supply.

[0031] Working principle: If the standard temperature for normal operation of the equipment is 25 degrees, the temperature sensor monitors the temperature of the equipment in real time. When the operating temperature of the equipment is 26 degrees, the processing value is determined to be 1. If there is an execution value of 1, the execution strategy can be to turn on a single fan for cooling. When the operating temperature of the equipment is 28 degrees, the processing value is determined to be 3. If there is an execution value of 3, the execution strategy can turn on multiple fans. If the operating temperature of the equipment is 35 degrees, it exceeds the first benchmark temperature value, but the difference does not exceed the first temperature difference. At this time, if the processing value has a corresponding execution value, the execution strategy can be to turn on all fans, turn off the power of the non-working equipment, and issue a warning.

[0032] The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A power distribution module control system based on big data, comprising a collection module (1), a processing module (2), a central controller (3) and a plurality of different types of monitoring devices (4), characterized in that: The acquisition module (1) comprises a plurality of acquisition units (11), wherein the acquisition units (11) are used to acquire working status information of different types of monitoring devices (4), obtain acquired data and send the acquired data to the processing module (2); The processing module (2) is provided with an information classification library, the information classification library is used to store a plurality of classification information, each of the classification information includes a plurality of benchmark comparison data and a corresponding processing value, the processing module (2) is provided with a processing strategy, when the processing strategy receives the collected data, according to the collected data result, compares the collected data with the benchmark comparison data from the information classification library to obtain the corresponding classification information and the corresponding processing value, and uploads the classification information and the corresponding processing value to the central controller (3); The central controller (3) is configured with an analysis strategy, the analysis strategy is used to analyze the classification information and the corresponding processing value, the central controller (3) is configured with a local database, the local database stores execution information, the execution information includes the classification information and the corresponding execution value, and the execution strategy; When the corresponding classification information exists in the local database, and in the classification information, the processing value is consistent with the execution value, the corresponding execution strategy is output, and the central controller (3) sends the execution strategy to the execution device, and the execution device works in response to the execution strategy; It also includes a local expert end and a cloud. When the corresponding classification information does not exist in the local database or the processing value under the classification information is inconsistent with the execution value, the classification information and the corresponding processing value are fed back to the local expert end, and simultaneously uploaded to the cloud with the processing strategy of the local expert end, and storage data is generated and stored in the storage database. The analysis strategy simultaneously indexes the local database and the storage database; The storage database includes abnormal data, and the abnormal data includes abnormal strategies and corresponding classification information, as well as corresponding processing values. The cloud is provided with a warning module, and the warning module includes a warning strategy. The warning module sends the abnormal data to the local expert end. The warning strategy analyzes the processing strategy of the local expert end and the abnormal strategy. When the abnormal strategy is consistent with the processing strategy of the local expert end, the warning strategy sends a warning signal to the local expert end to remind the local technical expert.

2. A power distribution module control system based on big data according to claim 1, characterized in that: The execution information includes a deviation range corresponding to each of the classification information. The analysis strategy compares the processing value with the execution value and outputs a first difference. The first difference represents the difference between the processing value and the execution value. When the processing value under the classification information is inconsistent with the execution value, if the first difference is within the deviation range, the corresponding execution strategy is output.

3. A power distribution module control system based on big data according to claim 2, characterized in that: The analysis strategy is preset with an expert processing time estimate. When the processing waiting time exceeds the expert processing time estimate, the analysis strategy starts to analyze whether the first difference is within the deviation range.

4. The power distribution module control system based on big data according to claim 1, characterized in that: The exception strategy includes an exception step, the processing strategy of the local expert end includes a processing step, the exception strategy compares the exception step with the processing step being input by the local expert end, and marks the part in the processing step that is repeated with the exception step.

5. The power distribution module control system based on big data according to claim 1, characterized in that: The monitoring device (4) includes a temperature sensor, the collected data includes a temperature value, the temperature value represents the temperature collected by the temperature sensor in real time, the benchmark comparison data includes a first benchmark temperature value and a second benchmark temperature value, the first benchmark temperature value is greater than the second benchmark temperature value, and when the temperature value is greater than the first benchmark temperature or the temperature value is less than the second benchmark temperature value, the execution strategy sends an alarm signal.

6. The power distribution module control system based on big data according to claim 5, characterized in that: The reference comparison data includes a first temperature difference and a second temperature difference. When the difference between the temperature value and the first reference temperature value is greater than the first temperature difference or the difference between the temperature value and the second reference temperature value is greater than the second temperature difference, the execution strategy cuts off power.

7. The power distribution module control system based on big data according to claim 1, characterized in that: The monitoring device (4) includes an AC voltage sensor, the collected data includes an AC voltage value, the AC voltage value represents the AC voltage collected by the AC voltage sensor in real time, the reference comparison data includes a first reference voltage value and a second reference voltage value, the first reference voltage value is greater than the second reference voltage value, and when the AC voltage value is greater than the first reference voltage value or the AC voltage value is less than the second reference voltage value, the execution strategy sends an alarm signal.

8. The power distribution module control system based on big data according to claim 7, characterized in that: The benchmark comparison data includes a first voltage difference and a second voltage difference. When the difference between the AC voltage value and the first benchmark voltage value is greater than the first voltage difference or the difference between the AC voltage value and the second benchmark voltage value is greater than the second voltage value, the execution strategy cuts off power.

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