PCS real-time online monitoring system based on intelligent gateway

Abstract

The invention particularly relates to a PCS real-time online monitoring system based on an intelligent gateway. The PCS real-time online monitoring system comprises the following parts: a multi-source information collection module for collecting temperature information data, cooling fan information data and ventilation information data in equipment; the information analysis module is used for analyzing the temperature information data to obtain a temperature evaluation coefficient; analyzing the information data of the cooling fan to obtain a fan aging coefficient; analyzing the ventilation information data to obtain a blockage coefficient; and a comprehensive diagnosis module. According to the invention, through the distributed temperature sensing network, fan state monitoring and airflow channel analysis, collaborative evaluation of the thermal state, the mechanical performance and the ventilation efficiency of the equipment is realized; the limitation of the traditional single-point threshold alarm is broken through by combining the different-temperature proportion, the duration and the temperature difference fluctuation; rotation speed deviation and noise characteristics are fused, and the heat dissipation failure caused by air duct blockage is effectively avoided through combination of cone model quantitative aging degree pressure difference analysis and image recognition.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronic equipment monitoring, and in particular to a PCS real-time online monitoring system based on an intelligent gateway. Background Art

[0002] With the rapid development of new energy power generation, electric vehicle charging and other fields, PCS, as the core device of energy conversion, its reliability directly affects the system's operating efficiency and safety. Traditional monitoring methods have the following technical bottlenecks:

[0003] Existing technologies rely on manual inspections or periodic shutdowns for testing, failing to capture dynamic hazards during equipment operation. For example, abnormal junction temperature in an IGBT module can cause device failure within minutes, but offline testing cycles typically take months. Existing monitoring systems often focus on a single temperature metric, but factors like cooling fan aging and air duct blockages also affect equipment lifespan. Existing assessment methods rely on fixed thresholds, but factors like equipment aging and environmental changes can cause thresholds to fail.

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

[0005] The purpose of the present invention is to solve the above problems and to propose a PCS real-time online monitoring system based on an intelligent gateway.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A PCS real-time online monitoring system based on intelligent gateway includes the following parts:

[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 temperature evaluation coefficient; analyzes cooling fan information data to obtain fan aging coefficient; analyzes ventilation information data to obtain blockage coefficient;

[0010] Comprehensive diagnosis module: The obtained temperature assessment coefficient, fan aging coefficient, and blockage coefficient are comprehensively analyzed to obtain the risk coefficient; 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 in the collection device specifically include:

[0012] Temperature information data: randomly select a preset number of components in the equipment as key hot spots, and place sensors at the key hot spots to obtain temperature information data of each key hot spot;

[0013] Cooling fan information data: obtain fan speed data and noise decibel data through sensors;

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

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

[0016] Acquire temperature values ​​of key heating points at preset time intervals, preset an allowable temperature range for the key heating points during normal operation, compare the acquired temperature values ​​of the key heating points with the allowable temperature range during normal operation in sequence, record temperatures that are not within the allowable temperature range during normal operation as abnormal temperatures, and count all abnormal temperatures; divide all abnormal temperature values ​​by the total number of temperature values ​​of the key heating points to obtain an abnormal temperature ratio;

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

[0018] Arrange the temperature values ​​of the key hot spots obtained from left to right in the order of their acquisition time, and subtract the previous temperature value from the next temperature value in the time sequence to obtain the absolute value, and obtain the temperature difference value of the adjacent time intervals; preset the allowable range of temperature difference, record the temperature difference value that is not within the allowable range as abnormal temperature difference, and calculate the average of all abnormal temperature differences to obtain the abnormal mean difference;

[0019] Substitute the abnormal temperature ratio, abnormal temperature duration, and abnormal mean difference of the key heating point into the corresponding formula to calculate the key coefficient;

[0020] After evaluating the importance of each key heating point, a corresponding weight factor is assigned, and the key coefficient of each key heating point is multiplied by the corresponding weight factor and then summed to obtain the temperature evaluation coefficient.

[0021] Preferably, the fan aging coefficient is obtained after analyzing the cooling fan information data, which specifically includes the following parts:

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

[0023] Set the allowed fluctuation range of the standard speed difference. If the standard speed difference is not within the allowed fluctuation range, the standard speed difference is marked 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, and the difference between the maximum deviation standard deviation value and the minimum deviation standard deviation value in the running time is calculated to obtain the deviation range value;

[0025] Any moment that generates a deviation from the standard deviation value within the running time is recorded as the first moment, and the moment that generates a deviation from the standard deviation value after the next moment 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 values ​​within the running time zone is calculated and the absolute value is taken to obtain the time deviation fluctuation value;

[0026] The weight factors of the deviation standard deviation, deviation range, and deviation time fluctuation are preset, and the deviation standard deviation, deviation range, and deviation time fluctuation are multiplied by their corresponding weight factors and then summed to obtain the speed abnormality value;

[0027] Obtain the decibel data of the cooling fan noise at each time point within a preset time period, and construct a cooling fan noise decibel change graph based on the data. The horizontal axis represents each time point within the preset time period, and the vertical axis represents the cooling fan noise decibel value corresponding to each time point. The cooling fan speed noise decibel corresponding to each time point is plotted in the change graph. The allowable noise decibel range of the cooling fan is preset, and a threshold line corresponding to the maximum allowable noise decibel value is plotted in the line graph.

[0028] Mark the numerical points above the threshold line, 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 them, and use the accumulated value as the noise outlier;

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

[0030] Preferably, the blockage coefficient is obtained after analyzing the ventilation information data, which specifically includes the following parts:

[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 in turn and take the absolute value to obtain the pressure difference value;

[0032] Preset an allowable range of pressure difference values, record a pressure difference value that is not within the allowable range of pressure difference values ​​as an abnormal pressure difference value, and mark two areas corresponding to the abnormal pressure difference value as abnormal areas;

[0033] Count the number of abnormal areas, and divide the total number of abnormal areas by the total number of adjacent areas to obtain the foreign body ratio;

[0034] A photo of the heat inlet image is magnified to obtain its pixel block image, the color values ​​of the heat inlet pixel blocks are identified, and averaged to obtain the heat inlet color deviation mean. The color deviation mean is compared with the heat inlet standard range color value at the angle corresponding to the set parameters. If the heat inlet color deviation from the mean is not within the heat inlet standard range color value, the color deviation from the mean is recorded as the color deviation difference mean one;

[0035] The average color deviation difference in the heat dissipation air inlet images at different angles is weighted to obtain the total color deviation value of the air inlet;

[0036] Obtain blockage information at the air outlet of the heat dissipation holes of the chassis server, and analyze the heat dissipation air outlet image to obtain the total color deviation value of the air outlet based on the above process of analyzing the heat dissipation air inlet image;

[0037] The weight factors of the foreign matter ratio, the air inlet color deviation total value, and the air outlet color deviation total value are preset. The foreign matter ratio, the air inlet color deviation total value, the air outlet color deviation total value and their corresponding weight factors are multiplied and summed to obtain the blockage coefficient.

[0038] Preferably, the risk coefficient is obtained by comprehensively analyzing the obtained temperature assessment coefficient, fan aging coefficient, and blockage coefficient, and 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 right-angled sides of a right triangle. The remaining side is connected to obtain a complete right triangle. The blockage coefficient is used as the height of the right triangle to establish a triangular pyramid model. The volume of the triangular pyramid model is calculated and recorded as the risk coefficient.

[0040] Preferably, the matching of the corresponding status level according to the risk coefficient and performing corresponding processing based on the status level specifically include the following parts:

[0041] When the status level corresponding to the risk factor is normal: the temperature information data, cooling fan data, and ventilation information data in the equipment are continuously collected at preset time intervals, and the obtained temperature assessment coefficient, fan aging coefficient, blockage coefficient, and risk coefficient are recorded together and regularly generated into a report. 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 the preset time;

[0042] When the status level corresponding to the risk factor is abnormal: on the basis that the level corresponding to the risk factor is normal, a level 1 sound and light alarm is issued; the abnormal data involved in the generated report is marked, and the time required for maintenance personnel to rush to the equipment location for inspection is shortened;

[0043] When the status level corresponding to the risk factor is emergency: on the basis that the level corresponding to the risk factor is normal, a second-level sound and light alarm is issued and the power supply of the equipment is immediately cut off; the maintenance personnel around the equipment are screened and the preferred personnel are obtained, and the report generated by the equipment is sent to the smart terminal of the preferred personnel. After checking the report on the smart terminal, the preferred personnel rushes to the equipment within the preset time to perform maintenance on the equipment.

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

[0045] After obtaining the location of the equipment, a circle is drawn on the map with the equipment location as the center and a preset radius. All maintenance personnel within the circle are obtained, and maintenance personnel who are within normal working hours are selected.

[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 with length of service lower than the minimum required length of service. Analyze the remaining maintenance personnel and obtain the processing value of each maintenance personnel in turn. Arrange the processing value of each maintenance personnel in descending order from left to right according to size, 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. The present invention achieves a coordinated assessment of the equipment's thermal status, mechanical performance, and ventilation efficiency through a distributed temperature sensing network, fan status monitoring, and airflow channel analysis. It also breaks through the limitations of traditional single-point threshold alarms by combining the ratio of different temperatures, duration, and temperature difference fluctuations. It also integrates speed deviation and noise characteristics, quantifies the degree of aging through a cone model, and combines pressure difference analysis with image recognition to effectively avoid heat dissipation failures caused by air duct blockage.

[0049] 2. The present invention divides the status levels by three-dimensional risk factors. Normal status only requires regular inspections, abnormal status triggers targeted warnings, and emergency status automatically cuts off power and prioritizes the dispatch of highly skilled maintenance personnel. The processing value is calculated by completing the ratio and repairing the ratio, which reduces the average response time and improves the utilization rate of maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0051] Figure 1 is a flow chart of the present invention; DETAILED DESCRIPTION

[0052] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

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

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

[0055] A PCS real-time online monitoring system based on intelligent gateway includes the following parts:

[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 temperature evaluation coefficient; analyzes cooling fan information data to obtain fan aging coefficient; analyzes ventilation information data to obtain blockage coefficient;

[0058] Collects temperature information data, cooling fan information data, and ventilation information data within the device, including:

[0059] Temperature information data: A preset number of components in the equipment are randomly selected as key hot spots, and sensors are placed at these key hot spots to obtain temperature information data of each key hot spot. Key hot spots include IGBT modules, inductor windings, capacitor banks, etc.

[0060] Cooling fan information data: obtain fan speed data and noise decibel data through sensors;

[0061] Ventilation information data: The equipment's air outlet and air inlet ducts are divided into zones at preset length intervals. Pressure sensors are placed in each zone, and the values ​​of each pressure sensor are obtained at preset time intervals to monitor pressure changes in that zone. Image information of the equipment's heat dissipation inlet and outlet is also obtained.

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

[0063] Acquire temperature values ​​of key heating points at preset time intervals, preset an allowable temperature range for the key heating points during normal operation, compare the acquired temperature values ​​of the key heating points with the allowable temperature range during normal operation in sequence, record temperatures that are not within the allowable temperature range during normal operation as abnormal temperatures, and count all abnormal temperatures; divide all abnormal temperature values ​​by the total number of temperature values ​​of the key heating points to obtain an abnormal temperature ratio;

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

[0065] Arrange the temperature values ​​of the key hot spots obtained from left to right in the order of their acquisition time, and subtract the previous temperature value from the next temperature value in the time sequence to obtain the absolute value, and obtain the temperature difference value of the adjacent time intervals; preset the allowable range of temperature difference, record the temperature difference value that is not within the allowable range as abnormal temperature difference, and calculate the average of all abnormal temperature differences to obtain the abnormal mean difference;

[0066] Substitute the abnormal temperature ratio, abnormal temperature duration, and abnormal mean difference of the key heating point into the corresponding formula to calculate the key coefficient;

[0067] After evaluating the importance of each key heating point, a corresponding weight factor is assigned, and the key coefficient of each key heating point is multiplied by the corresponding weight factor and then summed to obtain the temperature evaluation coefficient;

[0068] The calculation process is as follows: the abnormal temperature ratio, abnormal temperature duration, and abnormal mean difference are marked as 、 、 ;in is the number of the key hot spot, ;

[0069] The temperature ratio , duration of different temperatures , abnormal mean difference Substituting into the formula:

[0070] , get the key coefficient ;in 、 、 The key hot spots are The maximum allowable abnormal temperature ratio, the allowable duration of abnormal temperature, and the maximum allowable abnormal mean difference corresponding to the time; 、 、 are the weight factors corresponding to the abnormal temperature ratio, abnormal temperature duration, and abnormal mean difference respectively;

[0071] After analyzing the cooling fan information data, the fan aging coefficient is obtained, which specifically includes the following parts:

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

[0073] Set the allowed fluctuation range of the standard speed difference. If the standard speed difference is not within the allowed fluctuation range, the standard speed difference is marked 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, and the difference between the maximum deviation standard deviation value and the minimum deviation standard deviation value in the running time is calculated to obtain the deviation range value;

[0075] Any moment that generates a deviation from the standard deviation value within the running time is recorded as the first moment, and the moment that generates a deviation from the standard deviation value after the next moment 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 values ​​within the running time zone is calculated and the absolute value is taken to obtain the time deviation fluctuation value;

[0076] The weight factors of the deviation standard deviation, deviation range, and deviation time fluctuation are preset, and the deviation standard deviation, deviation range, and deviation time fluctuation are multiplied by their corresponding weight factors and then summed to obtain the speed abnormality value;

[0077] Obtain the decibel data of the cooling fan noise at each time point within a preset time period, and construct a cooling fan noise decibel change graph based on the data. The horizontal axis represents each time point within the preset time period, and the vertical axis represents the cooling fan noise decibel value corresponding to each time point. The cooling fan speed noise decibel corresponding to each time point is plotted in the change graph. The allowable noise decibel range of the cooling fan is preset, and a threshold line corresponding to the maximum allowable noise decibel value is plotted in the line graph.

[0078] Mark the numerical points above the threshold line, 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 them, and use the accumulated value as the noise outlier;

[0079] After normalizing the speed anomaly value and the noise anomaly value, a cone model is built with the speed anomaly value as the radius and the noise anomaly value as the height. 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 parts:

[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 in turn and take the absolute value to obtain the pressure difference value;

[0082] Preset an allowable range of pressure difference values, record a pressure difference value that is not within the allowable range of pressure difference values ​​as an abnormal pressure difference value, and mark two areas corresponding to the abnormal pressure difference value as abnormal areas;

[0083] Count the number of abnormal areas, and divide the total number of abnormal areas by the total number of adjacent areas to obtain the foreign body ratio;

[0084] A photo of the heat inlet image is magnified to obtain its pixel block image, the color values ​​of the heat inlet pixel blocks are identified, and averaged to obtain the heat inlet color deviation mean. The color deviation mean is compared with the heat inlet standard range color value at the angle corresponding to the set parameters. If the heat inlet color deviation from the mean is not within the heat inlet standard range color value, the color deviation from the mean is recorded as the color deviation difference mean one;

[0085] The average color deviation difference in the heat dissipation air inlet images at different angles is weighted to obtain the total color deviation value of the air inlet;

[0086] Obtain blockage information at the air outlet of the heat dissipation holes of the chassis server, and analyze the heat dissipation air outlet image to obtain the total color deviation value of the air outlet based on the above process of analyzing the heat dissipation air inlet image;

[0087] Preset weighting factors for the foreign matter ratio, the inlet color deviation value, and the outlet color deviation value. Multiply the foreign matter ratio, the inlet color deviation value, the outlet color deviation value, and their corresponding weighting factors and sum them to obtain the blockage coefficient.

[0088] Comprehensive diagnosis module: The obtained temperature assessment coefficient, fan aging coefficient, and blockage coefficient are comprehensively analyzed to obtain the risk coefficient; the corresponding status level is matched according to the risk coefficient, and corresponding processing is performed based on the status level;

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

[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 right-angled sides of a right triangle. The remaining side is connected to form a complete right triangle. The blockage coefficient is used as the height of the right triangle to establish a triangular pyramid model. The volume of the triangular pyramid model is calculated and recorded as the risk coefficient.

[0091] Three groups of threshold value ranges are preset, each group of threshold value ranges corresponds to a status level, and the obtained risk coefficient is matched with the three groups of threshold value ranges to obtain the status level corresponding to the risk coefficient; the status levels include normal, abnormal, and emergency;

[0092] Match the corresponding status level according to the risk factor and perform corresponding processing based on the status level, which specifically includes the following parts:

[0093] When the status level corresponding to the risk factor is normal: the device continuously collects temperature information data, cooling fan data, and ventilation information data at preset time intervals, records the obtained temperature assessment coefficient, fan aging coefficient, blockage coefficient, and risk coefficient, and generates reports regularly. The generated reports are sent to the smart terminals of relevant maintenance personnel at preset time intervals. After receiving the reports, the maintenance personnel inspect the equipment within the preset time.

[0094] When the status level corresponding to the risk factor is abnormal: on the basis that the level corresponding to the risk factor is normal, a level 1 sound and light alarm is issued; the abnormal data involved in the generated report is marked, and the time required for maintenance personnel to rush to the equipment location for inspection is shortened;

[0095] When the status level corresponding to the risk factor is emergency: on the basis that the level corresponding to the risk factor is normal, a second-level sound and light alarm is issued and the power supply of the equipment is immediately cut off; the maintenance personnel around the equipment are screened and the preferred personnel are selected, and the report generated by the equipment is sent to the smart terminal of the preferred personnel. After checking the report on the smart terminal, the preferred personnel rush to the equipment within the preset time to perform maintenance on the equipment;

[0096] When the status levels corresponding to the risk factors are normal, abnormal, and emergency, the preset time for maintenance personnel to rush to the equipment for maintenance decreases in sequence;

[0097] The alarm modes of the first-level sound and light alarm and the second-level sound and light alarm are:

[0098] Level 1 sound and light alarm, the light is blue, flashes several times per second, and emits a beep tone at a preset frequency and for a preset duration;

[0099] Second level sound and light alarm, the light is yellow, flashing several times per second at a multiple of the first level sound and light alarm, and the buzzer tone is several times the frequency and duration of the first level sound and light alarm;

[0100] For example: "Level 1 alarm: blue light flashes at 1Hz, beeps at 500Hz for 0.5 seconds; Level 2 alarm: yellow light flashes at 3Hz, beeps at 1000Hz for 1 second;

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

[0102] After obtaining the location of the equipment, a circle is drawn on the map with the equipment location as the center and a preset radius. All maintenance personnel within the circle are obtained, and maintenance personnel who are within normal working hours are selected.

[0103] 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 those with length of service below the minimum required length of service. Analyze the remaining maintenance personnel and obtain the processing value of each maintenance personnel in turn. Arrange the processing values ​​of each maintenance personnel in descending order from left to right, and select the maintenance personnel corresponding to the largest 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 zone before the current time point, as well as the handling time of each incident; preset an allowable range of incident handling time, compare the handling time of each incident by maintenance personnel with the allowable range of incident handling time in turn, and record the incident handling time within the allowable range of incident handling time as efficient time; count the efficient time of each maintenance personnel in turn, and divide each efficient time by the number of incidents handled by each maintenance personnel to obtain the completion ratio of each maintenance personnel;

[0106] Obtain the number of inverter power supplies that failed again within a preset time interval after each maintenance personnel handled each accident, and divide the number of inverter power supplies that failed again by each maintenance personnel by the number of accidents handled by each maintenance personnel to obtain the repair ratio of each maintenance personnel;

[0107] The weight factors of the completion ratio and the repair ratio are preset, and the completion ratio and the repair ratio of each maintenance personnel are multiplied by the corresponding weight factors in turn and then summed up to obtain the processing value of each maintenance personnel.

[0108] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factors and specific coefficient values ​​in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0109] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A PCS real-time online monitoring system based on intelligent gateway, characterized in that: Includes the following sections: Multi-source information collection module: collects temperature information data, cooling fan information data, and ventilation information data within the equipment; Information analysis module: Analyzes temperature information data to obtain temperature evaluation coefficient; analyzes cooling fan information data to obtain fan aging coefficient; analyzes ventilation information data to obtain blockage coefficient; Comprehensive diagnosis module: The obtained temperature assessment coefficient, fan aging coefficient, and blockage coefficient are comprehensively analyzed to obtain the risk coefficient; 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.

2. The PCS real-time online monitoring system based on an intelligent gateway according to claim 1 is characterized in that: The temperature information data, cooling fan information data, and ventilation information data in the collection device specifically include: Temperature information data: randomly select a preset number of components in the equipment as key hot spots, and place sensors at the key hot spots to obtain temperature information data of each key hot spot; Cooling fan information data: obtain fan speed data and noise decibel data through sensors; Ventilation information data: The equipment's air outlet and air inlet ducts are divided into areas with preset length intervals, and pressure sensors are deployed in each area. The values ​​of each pressure sensor are obtained at preset time intervals to monitor the pressure changes in the area; and image information of the equipment's heat dissipation inlet and outlet are obtained.

3. The PCS real-time online monitoring system based on intelligent gateway according to claim 2 is characterized in that: The temperature information data is analyzed to obtain the temperature evaluation coefficient. Includes the following sections: Acquire temperature values ​​of key heating points at preset time intervals, preset an allowable temperature range for the key heating points during normal operation, compare the acquired temperature values ​​of the key heating points with the allowable temperature range during normal operation in sequence, record temperatures that are not within the allowable temperature range during normal operation as abnormal temperatures, and count all abnormal temperatures; divide all abnormal temperature values ​​by the total number of temperature values ​​of the key heating points to obtain an abnormal temperature ratio; 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 temperature values ​​of the key hot spots obtained from left to right in the order of their acquisition time, and subtract the previous temperature value from the next temperature value in the time sequence to obtain the absolute value to obtain the temperature difference between adjacent time intervals; The allowable range of temperature difference is preset, and the temperature difference value that is not within the allowable range is recorded as abnormal temperature difference. The average of all abnormal temperature differences is calculated to obtain the abnormal mean difference; The abnormal temperature ratio, abnormal temperature duration and abnormal mean difference of key heating points are substituted into the corresponding formula to calculate the key coefficient; the key coefficients of each key point are comprehensively processed to obtain the temperature evaluation coefficient.

4. The PCS real-time online monitoring system based on an intelligent gateway according to claim 2 is characterized in that: The fan aging coefficient is obtained by analyzing the cooling fan information data, which specifically includes the following parts: Get the speed of the cooling fan when the device is running; set the standard speed value when the cooling fan is started, calculate the difference between the speed value when the cooling fan is started and the standard speed value, and then take the absolute value to obtain the standard speed difference; Set the allowed fluctuation range of the standard speed difference. If the standard speed difference is not within the allowed fluctuation range, the standard speed difference is marked as deviating from the standard deviation. The time between the start-up time of the cooling fan and the current time is marked as the running time, and the difference between the maximum deviation standard deviation value and the minimum deviation standard deviation value in the running time is calculated to obtain the deviation range value; Any moment that generates a deviation from the standard deviation during the running time is recorded as the first moment, and the moment that generates a deviation from the standard deviation immediately after the first moment 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 differences within the operating time zone is calculated and the absolute value is taken to obtain the bias time fluctuation value; The weight factors of the deviation standard deviation, deviation range, and deviation time fluctuation are preset, and the deviation standard deviation, deviation range, and deviation time fluctuation are multiplied by their corresponding weight factors and then summed to obtain the speed abnormality value; Obtain the decibel data of the cooling fan noise at each time point within a preset time period, and construct a cooling fan noise decibel change graph based on the data. The horizontal axis represents each time point within the preset time period, and the vertical axis represents the cooling fan noise decibel value corresponding to each time point. The cooling fan speed noise decibel corresponding to each time point is plotted in the change graph. The allowable noise decibel range of the cooling fan is preset, and a threshold line corresponding to the maximum allowable noise decibel value is plotted in the line graph. Mark the numerical points above the threshold line, 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 add them up, and use the accumulated value as the noise outlier; After normalizing the speed anomaly and noise anomaly, a circle is drawn with the speed anomaly as the radius and the noise anomaly as the height to establish a cone model. The volume of the cone model is calculated and used as the fan aging coefficient.

5. The PCS real-time online monitoring system based on intelligent gateway according to claim 2 is characterized in that: The blockage coefficient is obtained by analyzing the ventilation information data, which specifically includes the following parts: 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 in turn and take the absolute value to obtain the pressure difference value; Preset an allowable range of pressure difference values, record a pressure difference value that is not within the allowable range of pressure difference values ​​as an abnormal pressure difference value, and mark two areas corresponding to the abnormal pressure difference value as abnormal areas; Count the number of abnormal areas, and divide the total number of abnormal areas by the total number of adjacent areas to obtain the foreign body ratio; The heat dissipation inlet and outlet are analyzed to obtain the inlet color deviation comprehensive value and the outlet color deviation comprehensive value respectively; The weight factors of the foreign matter ratio, the air inlet color deviation total value, and the air outlet color deviation total value are preset. The foreign matter ratio, the air inlet color deviation total value, the air outlet color deviation total value and their corresponding weight factors are multiplied and summed to obtain the blockage coefficient.

6. The PCS real-time online monitoring system based on intelligent gateway according to claim 5 is characterized in that: The analysis of the heat dissipation inlet and outlet to obtain the inlet color deviation comprehensive value and the outlet color deviation comprehensive value specifically includes: A photo of the heat inlet image is magnified to obtain its pixel block image, the color values ​​of the heat inlet pixel blocks are identified, and averaged to obtain the heat inlet color deviation mean. The color deviation mean is compared with the heat inlet standard range color value at the angle corresponding to the set parameters. If the heat inlet color deviation from the mean is not within the heat inlet standard range color value, the color deviation from the mean is recorded as the color deviation difference mean one; The average color deviation difference in the heat dissipation air inlet images at different angles is weighted to obtain the total color deviation value of the air inlet; Obtain the blockage information at the air outlet of the heat dissipation hole of the chassis server, analyze the heat dissipation air outlet image to obtain the total value of the air inlet color deviation based on the above process, and analyze the heat dissipation air outlet image to obtain the total value of the air outlet color deviation.

7. The PCS real-time online monitoring system based on intelligent gateway according to claim 1 is characterized in that: The risk factor is obtained by comprehensively analyzing the obtained temperature assessment coefficient, fan aging coefficient, and blockage coefficient, which specifically includes the following parts: 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 right-angled sides of a right triangle. The remaining side is connected to obtain a complete right triangle. The blockage coefficient is used as the height of the right triangle to establish a triangular pyramid model. The volume of the triangular pyramid model is calculated and recorded as the risk coefficient.

8. The PCS real-time online monitoring system based on intelligent gateway according to claim 1 is characterized in that: The risk factor is matched to the corresponding status level, and corresponding processing is performed based on the status level, specifically including the following parts: When the status level corresponding to the risk factor is normal: the device continuously collects temperature information data, cooling fan data, and ventilation information data at preset time intervals, records the obtained temperature assessment coefficient, fan aging coefficient, blockage coefficient, and risk coefficient, and generates reports regularly. The generated reports are sent to the smart terminals of relevant maintenance personnel at preset time intervals. After receiving the reports, the maintenance personnel inspect the equipment within the preset time. When the status level corresponding to the risk factor is abnormal: on the basis that the level corresponding to the risk factor is normal, a level 1 sound and light alarm is issued; Mark abnormal data in the generated reports and shorten the time it takes for maintenance personnel to rush to the equipment location for inspection; When the status level corresponding to the risk factor is emergency: on the basis that the level corresponding to the risk factor is normal, a second-level sound and light alarm is issued and the power supply of the equipment is immediately cut off; the maintenance personnel around the equipment are screened and the preferred personnel are obtained, and the report generated by the equipment is sent to the smart terminal of the preferred personnel. After checking the report on the smart terminal, the preferred personnel rushes to the equipment within the preset time to perform maintenance on the equipment.

9. The PCS real-time online monitoring system based on intelligent gateway according to claim 8 is characterized in that: The method for obtaining the preferred personnel includes the following parts: After obtaining the location of the equipment, a circle is drawn on the map with the equipment location as the center and a preset radius. All maintenance personnel within the circle are obtained, and maintenance personnel who are within normal working hours are selected. 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 with length of service lower than the minimum required length of service. Analyze the remaining maintenance personnel and obtain the processing value of each maintenance personnel in turn. Arrange the processing value of each maintenance personnel in descending order from left to right according to size, and select the maintenance personnel corresponding to the maximum processing value as the preferred personnel.

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