A PCS real-time online monitoring system based on an intelligent gateway

Abstract

The application is particularly a PCS real-time online monitoring system based on an intelligent gateway, comprising the following parts: a multi-source information collection module: collecting temperature information data, heat dissipation fan information data and ventilation information data in the equipment; an information analysis module: obtaining a temperature evaluation coefficient after analyzing the temperature information data; obtaining a fan aging coefficient after analyzing the heat dissipation fan information data; obtaining a blockage coefficient after analyzing the ventilation information data; a comprehensive diagnosis module. In the application, the distributed temperature sensing network, the fan state monitoring and the airflow channel analysis realize the cooperative evaluation of the equipment thermal state, the mechanical performance and the ventilation efficiency; the limitation of the traditional single-point threshold alarm is broken through in combination with the heterogeneous temperature ratio, the continuous time length and the temperature difference fluctuation; the aging degree is quantified through the conical model in combination with the speed deviation and the noise characteristics, the pressure difference analysis and the image recognition, so that the heat dissipation failure caused by the air duct blockage is effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of power electronic equipment monitoring technology, and in particular to a PCS real-time online monitoring system based on a smart gateway. Background Technology

[0002] With the rapid development of new energy power generation and electric vehicle charging, the reliability of the PCS (Precision Control System), as a core energy conversion device, directly affects the system's operating efficiency and safety. Traditional monitoring methods suffer from the following technical bottlenecks:

[0003] Current technologies rely on manual inspections or periodic shutdowns for testing, which cannot capture dynamic potential problems during equipment operation. For example, abnormal junction temperature of an IGBT module can lead to device failure within minutes, but offline testing cycles are typically measured in months. Existing monitoring systems often focus on a single indicator, but factors such as aging cooling fans and blocked air ducts also affect equipment lifespan. Current evaluation methods rely on fixed thresholds for judgment, but factors such as equipment aging and environmental changes can cause these thresholds to become invalid.

[0004] Therefore, a PCS real-time online monitoring system based on a smart gateway is needed to address the problems mentioned above. Summary of the Invention

[0005] The purpose of this invention is to provide a PCS real-time online monitoring system based on a smart gateway in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A PCS real-time online monitoring system based on a smart gateway includes the following components:

[0008] Multi-source information collection module: collects temperature information data, cooling fan information data, and ventilation information data within the equipment;

[0009] Information analysis module: Analyzes temperature information data to obtain a temperature evaluation coefficient; analyzes cooling fan information data to obtain a fan aging coefficient; analyzes ventilation information data to obtain a blockage coefficient.

[0010] Comprehensive diagnostic module: After comprehensively analyzing the acquired temperature assessment coefficient, fan aging coefficient, and blockage coefficient, a risk coefficient is obtained; the corresponding status level is matched according to the risk coefficient, and corresponding processing is performed based on the status level; the status levels include normal, abnormal, and emergency.

[0011] Preferably, the temperature information data, cooling fan information data, and ventilation information data collected within the device specifically include:

[0012] Temperature information data: Randomly select a preset number of components in the equipment as key heat points, and deploy sensors at the key heat points to obtain temperature information data of each key heat point;

[0013] Cooling fan information data: The fan speed and noise level data are obtained through sensors;

[0014] Ventilation information data: The air outlet and air inlet ducts of the equipment are divided into areas with a preset length interval. Pressure sensors are installed in each area, and the values ​​of each pressure sensor are acquired at preset time intervals to monitor the pressure changes in that area; as well as image information of the equipment's heat dissipation air inlet and outlet vents are acquired.

[0015] Preferably, the temperature evaluation coefficient obtained after analyzing the temperature information data specifically includes the following parts:

[0016] The temperature values ​​of key heating points are acquired at preset time intervals. The temperature range allowed for key heating points during normal operation is preset. The acquired temperature values ​​of key heating points are compared with the temperature range allowed during normal operation in sequence. Temperatures that are not within the temperature range allowed during normal operation are recorded as abnormal temperatures, and the number of all abnormal temperatures is counted. The abnormal temperature ratio is obtained by dividing the number of all abnormal temperatures by the total number of temperature values ​​of the key heating point.

[0017] Obtain the duration corresponding to the abnormal temperature, and accumulate the durations corresponding to each abnormal temperature to obtain the duration of the abnormal temperature;

[0018] The temperature values ​​of the key heat points are obtained and arranged from left to right in chronological order. The absolute value of each adjacent temperature value is obtained by subtracting the previous temperature value from the next temperature value in the chronological order. The temperature difference between adjacent time intervals is obtained by setting an allowable range for the temperature difference. Temperature differences outside the allowable range are recorded as abnormal temperature differences. The average of all abnormal temperature differences is calculated to obtain the abnormal average difference.

[0019] The critical coefficient is calculated by substituting the proportion of abnormal temperatures, duration of abnormal temperatures, and average difference of abnormalities at key heating points into the corresponding formulas.

[0020] After assessing the importance of each key heat source, assigning corresponding weight factors, and then multiplying the key coefficient of each key heat source with its corresponding weight factor and summing the results, the temperature assessment coefficient is obtained.

[0021] Preferably, the process of analyzing the cooling fan information data to obtain the fan aging coefficient specifically includes the following parts:

[0022] Obtain the speed value of the cooling fan when the device is running; set the standard speed value of the cooling fan when it is running; calculate the difference between the speed value of the cooling fan when it is running and the standard speed value, and take the absolute value to obtain the standard speed difference.

[0023] Set the allowable fluctuation range for the standard speed difference. If the standard speed difference is not within the allowable fluctuation range, mark the standard speed difference as deviating from the standard deviation.

[0024] The time between the start-up time of the cooling fan and the current time is marked as the running time. The difference between the maximum and minimum deviation standard deviations within the running time is calculated to obtain the deviation range.

[0025] Any moment within the running time that generates a deviation from the standard deviation is recorded as the first moment, and the moment immediately following it that generates a deviation from the standard deviation is recorded as the second moment. The difference between the first moment and the second moment is calculated to obtain the adjacent time difference value. The standard deviation of the adjacent time difference value within the running time zone is calculated and the absolute value is taken to obtain the time deviation fluctuation value.

[0026] Preset weighting factors for deviation from standard deviation, deviation from range, and time-varying fluctuation. Then, multiply the deviation from standard deviation, deviation from range, and time-varying fluctuation with their corresponding weighting factors and sum them to obtain the abnormal speed value.

[0027] Obtain the decibel data of cooling fan noise at each time point within a preset time period, and construct a cooling fan noise decibel change graph. The horizontal axis represents each time point within the preset time period, and the vertical axis represents the corresponding cooling fan noise decibel value at each time point. Plot the numerical points in the graph corresponding to the cooling fan speed noise decibel at each time point. Preset the allowable range of cooling fan noise decibel, and plot the threshold line in the line graph corresponding to the highest allowable noise decibel value.

[0028] Mark the numerical points located above the threshold line, and extend a line segment perpendicular to the threshold line from each group of marked points, and mark it as the excess line; calculate the length of each excess line and accumulate them, and use the accumulated value as the noise anomaly value;

[0029] After normalizing the abnormal speed and noise values, a circle is drawn with the abnormal speed value as the radius and the abnormal noise value as the height to establish a conical model. The volume of the conical model is calculated and used as the fan aging coefficient.

[0030] Preferably, the process of analyzing ventilation information data to obtain the blockage coefficient specifically includes the following components:

[0031] Obtain the pressure values ​​corresponding to each area in the air outlet and air inlet ducts of the equipment, and calculate the difference between the pressure values ​​of adjacent areas and take the absolute value to obtain the pressure difference value.

[0032] The allowable range of pressure difference is preset. Pressure difference values ​​that are not within the allowable range are recorded as abnormal difference values, and the two regions corresponding to the abnormal difference values ​​are marked as abnormal regions.

[0033] The number of abnormal areas is counted, and the total number of abnormal areas is divided by the total number of adjacent areas to obtain the foreign object ratio.

[0034] The image of the heat dissipation air intake is enlarged to obtain its pixel block image. The color values ​​of the pixel blocks of the heat dissipation air intake are identified and averaged to obtain the color deviation of the heat dissipation air intake. The color deviation of the average value is compared with the standard range color value of the heat dissipation air intake at the corresponding angle of the set parameters. If the color of the heat dissipation air intake deviates from the average value and is not within the standard range of color values ​​of the heat dissipation air intake, the color deviation of the average value is recorded as the color deviation difference average value one.

[0035] The average color deviation difference in the images of the heat dissipation air inlet from different angles is weighted to obtain the comprehensive color deviation value of the air inlet.

[0036] Information on the blockage of the air outlet of the heat dissipation hole of the server chassis is obtained. Based on the above analysis of the heat dissipation air inlet image, the color deviation value of the air inlet is obtained. The color deviation value of the air outlet is obtained by analyzing the heat dissipation air outlet image.

[0037] The weighting factors for the foreign object ratio, the inlet color deviation from the overall value, and the outlet color deviation from the overall value are preset. The blockage coefficient is obtained by multiplying the foreign object ratio, the inlet color deviation from the overall value, and the outlet color deviation from the overall value with their corresponding weighting factors.

[0038] Preferably, the risk coefficient obtained by comprehensively analyzing the acquired temperature assessment coefficient, fan aging coefficient, and blockage coefficient specifically includes the following parts:

[0039] After normalizing the temperature assessment coefficient, fan aging coefficient, and blockage coefficient, the temperature assessment coefficient and fan aging coefficient are used as the two legs of a right triangle. The remaining leg is then connected to form a complete right triangle. The blockage coefficient is used as the height of this right triangle to establish a triangular pyramid model. The volume of this triangular pyramid model is calculated and recorded as the risk coefficient.

[0040] Preferably, the step of matching the corresponding state level based on the risk coefficient and performing corresponding processing based on the state level specifically includes the following parts:

[0041] When the status level corresponding to the risk coefficient is normal: continuously collect temperature information data, cooling fan data, and ventilation information data in the equipment at preset time intervals, and record the obtained temperature assessment coefficient, fan aging coefficient, blockage coefficient, and risk coefficient together to generate a report periodically. The generated report is sent to the smart terminal of the relevant maintenance personnel at preset time intervals. After receiving the report, the maintenance personnel will inspect the equipment within a preset time.

[0042] When the status level corresponding to the risk coefficient is abnormal: on the basis that the level corresponding to the risk coefficient is normal, a level one audible and visual alarm is issued; the abnormal data involved in the generated report is marked, and the time for maintenance personnel to reach the equipment location for inspection is shortened;

[0043] When the risk factor corresponds to an emergency status level: on the basis that the risk factor corresponds to a normal level, a level two audible and visual alarm is issued and the power supply to the equipment is immediately cut off; after screening the maintenance personnel around the equipment, the preferred personnel are selected, and the report generated by the equipment is sent to the preferred personnel's smart terminal. After viewing the report on the smart terminal, the preferred personnel rush to the equipment to perform maintenance within a preset time.

[0044] Preferably, the method for obtaining the preferred personnel includes the following parts:

[0045] After obtaining the location of the equipment, draw a circle on the map with the location of the equipment as the center and a preset size as the radius, obtain all maintenance personnel within the circle, and filter out the maintenance personnel within the normal working hours.

[0046] Obtain the length of service of each screened maintenance personnel and preset the minimum required length of service. Compare the length of service of each maintenance personnel with the minimum required length of service and remove maintenance personnel whose length of service is lower than the minimum required length of service. Analyze the remaining maintenance personnel to obtain the processing value of each maintenance personnel in turn. Sort the processing values ​​of each maintenance personnel in descending order from left to right, and select the maintenance personnel corresponding to the maximum processing value as the preferred personnel.

[0047] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0048] 1. This invention achieves a collaborative assessment of equipment thermal status, mechanical performance, and ventilation efficiency through a distributed temperature sensing network, fan status monitoring, and airflow channel analysis; it overcomes the limitations of traditional single-point threshold alarms by combining the ratio of different temperatures, duration, and temperature difference fluctuations; and it effectively avoids heat dissipation failure caused by airflow blockage by integrating speed deviation and noise characteristics and combining aging degree quantification pressure difference analysis with image recognition through a cone model.

[0049] 2. This invention classifies status levels by three-dimensional risk coefficients. In normal status, only periodic inspections are required. In abnormal status, targeted early warnings are triggered. In emergency status, power is automatically cut off and highly skilled maintenance personnel are prioritized for dispatch. By calculating the processing value through completion ratio and repair ratio, the average response time is reduced and the utilization rate of maintenance personnel is improved. Attached Figure Description

[0050] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0051] Figure 1 This is a flowchart of the present invention; Detailed Implementation

[0052] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0053] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0054] Please see Figure 1 As shown, the present invention provides a technical solution:

[0055] A PCS real-time online monitoring system based on a smart gateway includes the following components:

[0056] Multi-source information collection module: collects temperature information data, cooling fan information data, and ventilation information data within the equipment;

[0057] Information analysis module: Analyzes temperature information data to obtain a temperature evaluation coefficient; analyzes cooling fan information data to obtain a fan aging coefficient; analyzes ventilation information data to obtain a blockage coefficient.

[0058] Collect temperature, cooling fan, and ventilation data within the equipment, specifically including:

[0059] Temperature information data: A preset number of components within the equipment are randomly selected as key heat points, and sensors are deployed at these key heat points to obtain temperature information data for each key heat point; among these key heat points are IGBT modules, inductor windings, capacitor banks, etc.

[0060] Cooling fan information data: The fan speed and noise level data are obtained through sensors;

[0061] Ventilation information data: The air outlet and air inlet ducts of the equipment are divided into areas with a preset length interval. Pressure sensors are installed in each area, and the values ​​of each pressure sensor are acquired at preset time intervals to monitor the pressure changes in that area; as well as image information of the equipment's heat dissipation air inlet and outlet vents are acquired.

[0062] After analyzing the temperature information data, a temperature assessment coefficient is obtained, which specifically includes the following parts:

[0063] The temperature values ​​of key heating points are acquired at preset time intervals. The temperature range allowed for key heating points during normal operation is preset. The acquired temperature values ​​of key heating points are compared with the temperature range allowed during normal operation in sequence. Temperatures that are not within the temperature range allowed during normal operation are recorded as abnormal temperatures, and the number of all abnormal temperatures is counted. The abnormal temperature ratio is obtained by dividing the number of all abnormal temperatures by the total number of temperature values ​​of the key heating point.

[0064] Obtain the duration corresponding to the abnormal temperature, and accumulate the durations corresponding to each abnormal temperature to obtain the duration of the abnormal temperature;

[0065] The temperature values ​​of the key heat points are obtained and arranged from left to right in chronological order. The absolute value of each adjacent temperature value is obtained by subtracting the previous temperature value from the next temperature value in the chronological order. The temperature difference between adjacent time intervals is obtained by setting an allowable range for the temperature difference. Temperature differences outside the allowable range are recorded as abnormal temperature differences. The average of all abnormal temperature differences is calculated to obtain the abnormal average difference.

[0066] The critical coefficient is calculated by substituting the proportion of abnormal temperatures, duration of abnormal temperatures, and average difference of abnormalities at key heating points into the corresponding formulas.

[0067] After assessing the importance of each key heat source, assigning corresponding weight factors, and then summing the product of the key coefficient of each key heat source with its corresponding weight factor to obtain the temperature assessment coefficient;

[0068] The calculation process is as follows: The anomaly ratio, anomaly duration, and anomaly mean difference are respectively labeled as... , , ;in Numbering of key heat points ;

[0069] The ratio of different temperatures Duration of abnormal temperature Abnormal mean difference Substitute into the formula:

[0070] To obtain the key coefficient ;in , , The key heating points are as follows: The maximum permissible abnormal temperature ratio, the permissible duration of abnormal temperature, and the maximum permissible average difference of abnormality corresponding to the time; , , These are the weighting factors corresponding to the proportion of anomalous temperatures, the duration of anomalous temperatures, and the mean difference of anomalies, respectively.

[0071] The fan aging factor is obtained by analyzing the information data of the cooling fan, which includes the following components:

[0072] Obtain the speed value of the cooling fan when the device is running; set the standard speed value of the cooling fan when it is running; calculate the difference between the speed value of the cooling fan when it is running and the standard speed value, and take the absolute value to obtain the standard speed difference.

[0073] Set the allowable fluctuation range for the standard speed difference. If the standard speed difference is not within the allowable fluctuation range, mark the standard speed difference as deviating from the standard deviation.

[0074] The time between the start-up time of the cooling fan and the current time is marked as the running time. The difference between the maximum and minimum deviation standard deviations within the running time is calculated to obtain the deviation range.

[0075] Any moment within the running time that generates a deviation from the standard deviation is recorded as the first moment, and the moment immediately following it that generates a deviation from the standard deviation is recorded as the second moment. The difference between the first moment and the second moment is calculated to obtain the adjacent time difference value. The standard deviation of the adjacent time difference value within the running time zone is calculated and the absolute value is taken to obtain the time deviation fluctuation value.

[0076] Preset weighting factors for deviation from standard deviation, deviation from range, and time-varying fluctuation. Then, multiply the deviation from standard deviation, deviation from range, and time-varying fluctuation with their corresponding weighting factors and sum them to obtain the abnormal speed value.

[0077] Obtain the decibel data of cooling fan noise at each time point within a preset time period, and construct a cooling fan noise decibel change graph. The horizontal axis represents each time point within the preset time period, and the vertical axis represents the corresponding cooling fan noise decibel value at each time point. Plot the numerical points in the graph corresponding to the cooling fan speed noise decibel at each time point. Preset the allowable range of cooling fan noise decibel, and plot the threshold line in the line graph corresponding to the highest allowable noise decibel value.

[0078] Mark the numerical points located above the threshold line, and extend a line segment perpendicular to the threshold line from each group of marked points, and mark it as the excess line; calculate the length of each excess line and accumulate them, and use the accumulated value as the noise anomaly value;

[0079] After normalizing the abnormal speed and noise values, a circle is drawn with the abnormal speed value as the radius and the abnormal noise value as the height to establish a cone model. The volume of the cone model is calculated and used as the fan aging coefficient.

[0080] The blockage coefficient is obtained by analyzing the ventilation information data, which includes the following components:

[0081] Obtain the pressure values ​​corresponding to each area in the air outlet and air inlet ducts of the equipment, and calculate the difference between the pressure values ​​of adjacent areas and take the absolute value to obtain the pressure difference value.

[0082] The allowable range of pressure difference is preset. Pressure difference values ​​that are not within the allowable range are recorded as abnormal difference values, and the two regions corresponding to the abnormal difference values ​​are marked as abnormal regions.

[0083] The number of abnormal areas is counted, and the total number of abnormal areas is divided by the total number of adjacent areas to obtain the foreign object ratio.

[0084] The image of the heat dissipation air intake is enlarged to obtain its pixel block image. The color values ​​of the pixel blocks of the heat dissipation air intake are identified and averaged to obtain the color deviation of the heat dissipation air intake. The color deviation of the average value is compared with the standard range color value of the heat dissipation air intake at the corresponding angle of the set parameters. If the color of the heat dissipation air intake deviates from the average value and is not within the standard range of color values ​​of the heat dissipation air intake, the color deviation of the average value is recorded as the color deviation difference average value one.

[0085] The average color deviation difference in the images of the heat dissipation air inlet from different angles is weighted to obtain the comprehensive color deviation value of the air inlet.

[0086] Information on the blockage of the air outlet of the heat dissipation hole of the server chassis is obtained. Based on the above analysis of the heat dissipation air inlet image, the color deviation value of the air inlet is obtained. The color deviation value of the air outlet is obtained by analyzing the heat dissipation air outlet image.

[0087] The weighting factors for the foreign object ratio, the inlet color deviation from the overall value, and the outlet color deviation from the overall value are preset. The blockage coefficient is obtained by multiplying the foreign object ratio, the inlet color deviation from the overall value, and the outlet color deviation from the overall value with their corresponding weighting factors and then summing them.

[0088] Comprehensive diagnostic module: After comprehensively analyzing the acquired temperature assessment coefficient, fan aging coefficient, and blockage coefficient, a risk coefficient is obtained; the corresponding status level is matched according to the risk coefficient, and corresponding processing is performed based on the status level;

[0089] The risk coefficient is obtained by comprehensively analyzing the acquired temperature assessment coefficient, fan aging coefficient, and blockage coefficient, and specifically includes the following components:

[0090] After normalizing the temperature assessment coefficient, fan aging coefficient, and blockage coefficient, the temperature assessment coefficient and fan aging coefficient are used as the two legs of a right triangle. The remaining leg is then connected to form a complete right triangle. The blockage coefficient is used as the height of this right triangle to establish a triangular pyramid model. The volume of this triangular pyramid model is calculated and recorded as the risk coefficient.

[0091] Three threshold ranges are preset, each corresponding to a state level. The obtained risk coefficient is matched with the three threshold ranges to obtain the state level corresponding to the risk coefficient; the state levels include normal, abnormal, and emergency.

[0092] Match the corresponding state level based on the risk coefficient, and perform corresponding processing based on the state level, specifically including the following parts:

[0093] When the status level corresponding to the risk coefficient is normal: continuously collect temperature information data, cooling fan data, and ventilation information data in the equipment at preset time intervals, and record the obtained temperature assessment coefficient, fan aging coefficient, blockage coefficient, and risk coefficient together to generate a report periodically. The generated report is sent to the smart terminal of the relevant maintenance personnel at preset time intervals. After receiving the report, the maintenance personnel will inspect the equipment within a preset time.

[0094] When the status level corresponding to the risk coefficient is abnormal: on the basis that the level corresponding to the risk coefficient is normal, a level one audible and visual alarm is issued; the abnormal data involved in the generated report is marked, and the time for maintenance personnel to reach the equipment location for inspection is shortened;

[0095] When the risk factor corresponds to the emergency status level: on the basis that the risk factor corresponds to the normal level, issue a level two audible and visual alarm and immediately cut off the power supply to the equipment; screen the maintenance personnel around the equipment to select the best personnel, and send the report generated by the equipment to the smart terminal of the best personnel. After viewing the report on the smart terminal, the best personnel will rush to the equipment to perform maintenance within a preset time.

[0096] The risk coefficient corresponds to the status level of normal, abnormal, and emergency, with the preset time for maintenance personnel to rush to the equipment for maintenance decreasing in that order.

[0097] The alarm methods for Level 1 and Level 2 audible and visual alarms are as follows:

[0098] Level 1 audible and visual alarm: the light is blue and flashes several times per second, emitting a buzzer alert tone at a preset frequency and for a preset duration;

[0099] The level 2 audible and visual alarm uses a yellow light that flashes several times per second at a multiple of the level 1 audible and visual alarm frequency and emits a buzzer sound for a multiple of the level 1 audible and visual alarm duration.

[0100] For example: "Level 1 alarm: Blue light flashes at 1Hz, buzzer sounds at 500Hz for 0.5 seconds; Level 2 alarm: Yellow light flashes at 3Hz, buzzer sounds at 1000Hz for 1 second."

[0101] The methods for acquiring preferred personnel include the following:

[0102] After obtaining the location of the equipment, draw a circle on the map with the location of the equipment as the center and a preset size as the radius, obtain all maintenance personnel within the circle, and filter out the maintenance personnel within the normal working hours.

[0103] Obtain the years of service of each screened maintenance personnel and preset the minimum required years of service. Compare the years of service of each maintenance personnel with the minimum required years of service and remove maintenance personnel whose years of service are lower than the minimum required years of service. Analyze the remaining maintenance personnel and obtain the processing value of each maintenance personnel in turn. Sort the processing values ​​of each maintenance personnel in descending order from left to right and select the maintenance personnel corresponding to the maximum processing value as the preferred personnel.

[0104] The process of obtaining the processing value includes:

[0105] Obtain the number of incidents handled by maintenance personnel within a preset time range before the current time point, as well as the handling time for each incident; preset the allowable range for incident handling time, compare the handling time of each incident for each maintenance personnel with the allowable range, and record the incident handling time within the allowable range as the efficient time; calculate the efficient time for each maintenance personnel, and divide each efficient time by the number of incidents handled by each maintenance personnel to obtain the completion rate of each maintenance personnel;

[0106] The number of inverter power supplies that failed again within a preset time interval after each maintenance personnel handled each accident is obtained. The number of inverter power supplies that failed again for each maintenance personnel is then divided by the number of accidents handled by each maintenance personnel to obtain the repair ratio for each maintenance personnel.

[0107] The weighting factors for the completion and repair ratios are preset, and the completion and repair ratios of each maintenance worker are multiplied by their corresponding weighting factors and then summed to obtain the processing value for each maintenance worker.

[0108] The above formulas are derived from software simulations using a large amount of data and are selected to be close to the actual values. The influence weight factors and specific coefficient values ​​in the formulas are set by those skilled in the art based on the actual situation and can be adjusted and modified in the future.

[0109] The above description of the embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart gateway-based PCS real-time online monitoring system, characterized in that, Comprise the following parts: Multi-source information collection module: collect temperature information data, heat dissipation fan information data, ventilation information data in the device; Information analysis module: after analyzing the temperature information data, the temperature evaluation coefficient is obtained; after analyzing the heat dissipation fan information data, the fan aging coefficient is obtained; after analyzing the ventilation information data, the blockage coefficient is obtained; After analyzing the heat dissipation fan information data, the fan aging coefficient is obtained, which comprises the following parts: Get the speed value of the heat dissipation fan when the device is running; set the standard speed value in the starting state of the heat dissipation fan, take the absolute value of the difference value between the speed value in the starting state of the heat dissipation fan and the standard speed value, and get the standard speed difference value; Set the fluctuation range allowed by the standard speed difference value, if the standard speed difference value is not in the allowed fluctuation range, mark the standard speed difference value as a deviation standard difference value; Mark the time between the starting time of the heat dissipation fan and the current time as the running time, calculate the difference value between the maximum deviation standard difference value and the minimum deviation standard difference value in the running time, and get the deviation range value; Mark any time point generating deviation standard difference value in the running time as the first time, and mark the time point generating deviation standard difference value adjacent to it as the second time, calculate the difference value between the first time and the second time, and get the adjacent time difference value; Calculate the standard deviation of the adjacent time difference value in the running time zone and take the absolute value to get the time fluctuation value; Preset the weight factor of the deviation standard difference value, the deviation range value and the time fluctuation value, respectively multiply the deviation standard difference value, the deviation range value and the time fluctuation value by the corresponding weight factor, and then sum up to get the speed abnormal value; Get the noise decibel data of the heat dissipation fan at each time point in the preset time period, and construct the heat dissipation fan noise decibel change graph, the horizontal axis represents each time point in the preset time period, and the vertical axis represents the noise decibel value of the heat dissipation fan at each time point, draw the value point of the heat dissipation fan speed noise decibel corresponding in the change graph, preset the noise decibel allowed range of the heat dissipation fan, and draw the threshold line corresponding to the highest noise decibel allowed value in the line graph; Mark the value points above the threshold line, and extend a line segment perpendicular to the threshold line from each group of marked points as the starting point, and mark it as the exceeding line; Calculate the length of each exceeding line and accumulate, and take the accumulated value as the noise abnormal value; After normalizing the speed abnormal value and the noise abnormal value, draw a circle with the speed abnormal value as the radius and the noise abnormal value as the height, establish a conical model, calculate the volume of the conical model, and take the volume of the conical model as the fan aging coefficient; Comprehensive diagnosis module: after comprehensive analysis of the obtained temperature evaluation coefficient, fan aging coefficient and blockage coefficient, the risk coefficient is obtained; according to the risk coefficient, the corresponding state level is matched, and the corresponding processing is carried out based on the state level; The state level includes normal, abnormal and emergency. 2.The PCS real-time online monitoring system based on the intelligent gateway of claim 1, wherein, The collected temperature information data, heat dissipation fan information data and ventilation information data in the device specifically include: Temperature information data: randomly select a preset number of components in the device as key heat points, and arrange sensors at the key heat points to obtain temperature information data of each key heat point; Fan information data: obtain fan speed data and noise decibel data through sensors; Ventilation information data: divide the device's air outlet and air inlet ducts into regions at a preset length interval, arrange pressure sensors for each region, and obtain the values of each pressure sensor at a preset time interval to monitor the pressure changes in the region; and obtain image information of the device's heat dissipation air inlet and outlet. 3.The PCS real-time online monitoring system based on the intelligent gateway of claim 2, characterized in that, The temperature evaluation coefficient is obtained by analyzing the temperature information data, specifically including the following parts: obtain the temperature values of the key heat points at a preset time interval, preset the temperature range allowed during normal operation of the key heat points, compare the obtained temperature values of the key heat points with the temperature range allowed during normal operation, record the temperature that is not within the temperature range allowed during normal operation as abnormal temperature, and count the number of all abnormal temperatures; divide the number of all abnormal temperatures by the total number of temperature values of the key heat points to obtain the abnormal temperature proportion; obtain the duration corresponding to the abnormal temperature, and accumulate the duration corresponding to each abnormal temperature to obtain the abnormal temperature duration; arrange the obtained temperature values of the key heat points in the order of time from left to right, and subtract the previous temperature value from the subsequent temperature value in the adjacent temperature values in the order of time to obtain the temperature difference value of the adjacent time interval; preset the allowed range of temperature difference, record the temperature difference value that is not within the allowed range as abnormal temperature difference, and obtain the abnormal mean difference by mean calculation of all abnormal temperature differences; substitute the abnormal temperature proportion, abnormal temperature duration, and abnormal mean difference of the key heat points into the corresponding formula to obtain the key coefficient; and obtain the temperature evaluation coefficient by comprehensively processing the key coefficients of each key point. 4.The PCS real-time online monitoring system based on the intelligent gateway of claim 2, wherein, The clogging coefficient is obtained by analyzing the ventilation information data, specifically including the following parts: obtain the pressure values corresponding to each region in the device's air outlet and air inlet ducts, and obtain the pressure difference value by difference calculation and absolute value calculation of the pressure values of adjacent regions; preset the allowed range of pressure difference value, record the pressure difference value that is not within the allowed range of pressure difference value as abnormal difference value, and mark the two regions corresponding to the abnormal difference value as abnormal regions; count the number of abnormal regions, and divide the total number of abnormal regions by the total number of adjacent regions to obtain the foreign matter proportion; analyze the heat dissipation air inlet and outlet to obtain the air inlet color deviation comprehensive value and the air outlet color deviation comprehensive value, respectively; preset the weight factors of the foreign matter proportion, the air inlet color deviation comprehensive value, and the air outlet color deviation comprehensive value, respectively, multiply the foreign matter proportion, the air inlet color deviation comprehensive value, and the air outlet color deviation comprehensive value by their corresponding weight factors, and sum them up to obtain the clogging coefficient. 5.The PCS real-time online monitoring system based on the intelligent gateway of claim 4, wherein, The air inlet color deviation comprehensive value and the air outlet color deviation comprehensive value are obtained by analyzing the heat dissipation air inlet and outlet, specifically including: The heat dissipation air inlet image is enlarged to obtain a pixel block image, the pixel block color value of the heat dissipation air inlet is identified, and the mean value of the pixel block color value is obtained, and the color deviation mean value is obtained by comparing the color deviation mean value with the standard range color value of the heat dissipation air inlet at the corresponding angle. If the color deviation mean value is not within the standard range color value of the heat dissipation air inlet, the color deviation mean value is recorded as color deviation mean value one; The color deviation mean values in the heat dissipation air inlets at different angles are weighted to obtain the air inlet color deviation comprehensive value. The clogging condition information of the air outlet of the heat dissipation hole of the server is obtained, and the air outlet color deviation comprehensive value is obtained by analyzing the heat dissipation air outlet image based on the process of obtaining the air inlet color deviation comprehensive value by analyzing the heat dissipation air inlet image. 6.The PCS real-time online monitoring system based on the intelligent gateway of claim 1, wherein, The temperature evaluation coefficient, the fan aging coefficient and the clogging coefficient are obtained, and the risk coefficient is obtained by comprehensive analysis, which includes the following parts: After the temperature evaluation coefficient, the fan aging coefficient and the clogging coefficient are normalized, the temperature evaluation coefficient and the fan aging coefficient are used as two legs of a right triangle, and the remaining leg is connected to form a complete right triangle, and the clogging coefficient is used as the height of the right triangle, thereby establishing a three-prism model, calculating the volume of the three-prism model, and recording the volume as the risk coefficient. 7.The PCS real-time online monitoring system based on the intelligent gateway of claim 1, wherein, The corresponding state level is matched according to the risk coefficient, and corresponding processing is carried out based on the state level, which includes the following parts: When the state level corresponding to the risk coefficient is normal: the temperature information data, the heat dissipation fan data and the ventilation information data in the equipment are continuously collected at a preset time interval, and the temperature evaluation coefficient, the fan aging coefficient, the clogging coefficient and the risk coefficient are recorded together to generate a report at a regular interval, and the generated report is sent to the intelligent terminal of the related maintenance personnel at a preset time interval, and the maintenance personnel checks the equipment within a preset time after receiving the report; When the state level corresponding to the risk coefficient is abnormal: on the basis that the risk coefficient corresponds to the normal level, a first sound and light alarm is issued; The abnormal data involved in the generated report is marked, and the time for the maintenance personnel to rush to the equipment site for inspection is shortened; When the state level corresponding to the risk coefficient is urgent: on the basis that the risk coefficient corresponds to the normal level, a second sound and light alarm is issued at the same time as the power supply of the equipment is immediately cut off; the preferred personnel are obtained by screening the maintenance personnel around the equipment, and the report generated by the equipment is sent to the intelligent terminal of the preferred personnel, and the preferred personnel rush to the equipment for maintenance treatment within a preset time after checking the report on the intelligent terminal. 8.The PCS real-time online monitoring system based on the intelligent gateway of claim 7, wherein, The preferred personnel are obtained by the following parts: After the location of the equipment is obtained, a circle is drawn on the map with the location of the equipment as the center and a preset size as the radius, all maintenance personnel within the circular range are obtained, and the maintenance personnel within the normal working time range are screened out. The service life of each screened maintenance personnel is obtained, and a minimum required service life is preset. The service life of each maintenance personnel is compared with the minimum required service life, and the maintenance personnel with a service life lower than the minimum required service life is removed. The remaining maintenance personnel is analyzed to obtain a processing value of each maintenance personnel in sequence. The processing values of the maintenance personnel are arranged in descending order from left to right according to size, and the maintenance personnel corresponding to the maximum processing value is selected as the preferred personnel.

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