A monitoring method and system of an intelligent power distribution cabinet

Abstract

The application relates to a monitoring method and system of an intelligent power distribution cabinet, and belongs to the technical field of intelligent monitoring. The method comprises the following steps: acquiring an instrument data image of the power distribution cabinet; acquiring instrument data based on the instrument data image; judging whether the instrument data matches preset normal data; if the instrument data does not match the normal data, acquiring an infrared image of the power distribution cabinet; judging whether an abnormality occurs in the power distribution cabinet based on the infrared image; if the abnormality occurs, acquiring abnormal information of the power distribution cabinet and outputting the abnormal information; if the abnormality does not occur, determining that an instrument abnormality occurs, acquiring instrument abnormal information and outputting the instrument abnormal information. The application has the effect of improving the monitoring effect on the power distribution cabinet.

Description

Technical Field

[0001] This application relates to the technical field of intelligent monitoring, and in particular to a monitoring method and system for an intelligent power distribution cabinet. Background Technology

[0002] A distribution cabinet is a power distribution device used in a power supply system for distributing, controlling, metering, and connecting cables. It contains various electrical components and wiring, and integrates switches, circuit breakers, fuses, indicator lights, and other parts to perform various electrical functions.

[0003] Because power distribution cabinets typically carry high-voltage electricity, they are often placed in relatively enclosed locations to minimize the risk of electric shock. To monitor the status of the distribution cabinets, high-definition cameras are usually installed around them to monitor the operating environment and data.

[0004] Regarding the aforementioned technologies, the inventors believe that monitoring the operation of the power distribution cabinet using the above methods is relatively weak. Summary of the Invention

[0005] To improve the monitoring effect of power distribution cabinets, this application provides a monitoring method and system for intelligent power distribution cabinets.

[0006] Firstly, this application provides a monitoring method for an intelligent power distribution cabinet, employing the following technical solution:

[0007] A monitoring method for an intelligent power distribution cabinet includes:

[0008] Acquire instrument data images from the power distribution cabinet;

[0009] Obtain instrument data based on the instrument data image;

[0010] Determine whether the instrument data matches the preset normal data;

[0011] If there is no match, then the infrared image of the power distribution cabinet is obtained;

[0012] Based on the infrared image, determine whether an abnormality has occurred inside the power distribution cabinet;

[0013] If an abnormality occurs, obtain and output the abnormality information of the power distribution cabinet;

[0014] If no abnormality occurs, the instrument is determined to be abnormal, and the abnormality information is obtained and output.

[0015] By employing the above technical solution, instrument data is acquired based on the instrument data image, and its match with normal data is determined to identify any anomalies. If a mismatch is found, an anomaly is identified. In this case, an infrared image is acquired, and the internal structure of the distribution cabinet is assessed to determine if the anomaly is caused by an internal fault. If an anomaly is found, it is highly likely that the anomaly is caused by the distribution cabinet itself; in this case, the anomaly information is acquired and output. If no anomaly is found, it is highly likely that the anomaly is caused by the instrument itself; in this case, the anomaly information is acquired and output. By switching between instrument data images and infrared images, faults in the distribution cabinet can be confirmed, thereby improving the monitoring effectiveness of the distribution cabinet.

[0016] Preferably, after determining the instrument malfunction, acquiring the instrument malfunction information, and outputting it, the method further includes:

[0017] Obtain the theoretical operating time for which the instrument can work normally;

[0018] Obtain the current operating time of the instrument;

[0019] Determine whether the current working time exceeds the theoretical working time;

[0020] If the error exceeds the limit, the instrument will be output as a normal damage condition.

[0021] If the error does not exceed the limit, the instrument will be output as an abnormal damage.

[0022] By employing the above technical solution, it can be determined whether the current working time exceeds the theoretical working time, thus indicating whether the instrument has exceeded its normal working life. If it does, it proves that the instrument is likely damaged under normal conditions, and the output instrument abnormality information indicates normal damage. If it does not exceed the limit, it proves that the instrument is likely damaged by external factors, and the output instrument abnormality information indicates abnormal damage. Therefore, this method can further determine the cause of instrument damage and improve the accuracy of monitoring and judgment.

[0023] Preferably, the step of outputting the instrument malfunction information as abnormal damage includes the following steps:

[0024] Obtain the vibration parameters of the power distribution cabinet;

[0025] Determine whether the vibration parameter is greater than the parameter threshold;

[0026] If so, the abnormal damage output is vibration damage;

[0027] If not, the output of the abnormal damage is self-damage.

[0028] By employing the above technical solution, determining whether the vibration parameter exceeds the threshold value can help determine if the instrument malfunction is caused by vibration. If so, i.e., the vibration parameter exceeds the threshold value, it is highly likely that the instrument is damaged by vibration, and the abnormal output is due to vibration damage. If not, it is highly likely that the instrument itself is faulty, and the abnormal output is due to its own inherent characteristics. This method further improves the accuracy of determining the cause of power distribution cabinet malfunctions, facilitating targeted maintenance by workers.

[0029] Preferably, the output includes the following step before the abnormal damage is itself:

[0030] Obtain the current humidity value inside the power distribution cabinet;

[0031] Determine whether the current humidity value is greater than a humidity threshold, and obtain a first determination result;

[0032] If the first judgment result is yes, then output a message indicating that the product is damaged due to moisture.

[0033] If the first judgment result is negative, proceed to the next step.

[0034] By employing the above technical solution, the system determines whether the current humidity value exceeds a humidity threshold and obtains a first judgment result, enabling it to determine whether the instrument malfunction is caused by moisture. If the first judgment result is yes, it indicates that the instrument is likely damaged by moisture, and a moisture-related damage warning is output. If the first judgment result is no, the next step is to output that the abnormal damage is due to the instrument's own inherent defects. This allows for further analysis of the cause of the instrument malfunction, thereby improving the accuracy of the judgment.

[0035] Preferably, the following is included after the output moisture damage warning:

[0036] A heating command is obtained, and the heater in the distribution cabinet is controlled to heat the current humidity value to be less than or equal to the humidity threshold.

[0037] By adopting the above technical solution, when the first judgment result is yes, a heating command is obtained, and the heater is controlled to heat the distribution cabinet according to the heating command, so that the current humidity value is less than or equal to the temperature threshold, thereby ensuring the dryness of the distribution cabinet as much as possible, and thus improving the safety of the distribution cabinet during operation.

[0038] Preferably, after obtaining and outputting the power distribution cabinet abnormality information if an abnormality occurs, the method further includes:

[0039] The location of the anomaly was determined based on the infrared image;

[0040] Obtain the location of the electrical appliances inside the power distribution cabinet;

[0041] The corresponding appliance type is obtained based on the abnormal location and the appliance location;

[0042] Based on the type of electrical appliance, obtain and output the fault cause information.

[0043] By adopting the above technical solution, the corresponding electrical appliance type can be obtained based on the abnormal location and the electrical appliance location. At this time, the cause of the fault can be obtained and output based on the electrical appliance type, and the abnormal electrical appliance can be found. Thus, the type of cause of the abnormality can be determined based on the abnormal electrical appliance, which makes it easier for maintenance personnel to investigate the cause of the fault and improves the convenience of maintenance.

[0044] Preferably, after obtaining and outputting the fault cause information based on the appliance type, the method further includes:

[0045] Obtain the fan status of the cooling fan inside the power distribution cabinet;

[0046] Based on the fan status, determine whether the cooling fan is functioning normally and obtain a second determination result;

[0047] If the second judgment result is yes, then the output fault cause information is a fault of the equipment itself;

[0048] If the second judgment result is negative, the output fault cause information is cooling fan failure.

[0049] By adopting the above technical solution, the status of the cooling fan is determined based on its condition, and a second judgment result is obtained. If the second judgment result is yes, it proves that the cooling fan is normal, and it is highly likely that the fault is caused by the equipment itself. In this case, the output fault cause is a fault in the equipment itself. If the second judgment result is no, it proves that the equipment fault may be caused by a failure of the cooling fan, and the output fault cause is a failure of the cooling fan. This method can further improve the accuracy of determining the cause of equipment faults.

[0050] Preferably, the output fault cause information is a device fault, which includes the following steps:

[0051] Obtain an image of the heat dissipation vents of the power distribution cabinet;

[0052] Determine whether the heat dissipation vents of the power distribution cabinet are normal based on the image of the heat dissipation vents;

[0053] If not, the output fault cause information is a heat dissipation port failure;

[0054] If so, the output fault cause information is a fault in the device itself.

[0055] By adopting the above technical solution, when the second judgment result is yes, the system determines whether the heat dissipation vents of the distribution cabinet are normal based on the vent image. If yes, it proves that the heat dissipation vents are normal, and the fault is most likely due to the equipment itself; the output fault cause is then determined to be a problem with the equipment itself. If no, it proves that the abnormal temperature of the equipment is most likely caused by an abnormal heat dissipation vent, and the output fault cause is then determined to be a heat dissipation vent fault. This allows for a more specific output of the fault cause, making it easier for maintenance personnel to perform repairs.

[0056] Secondly, this application provides a monitoring system for an intelligent power distribution cabinet, which adopts the following technical solution:

[0057] A monitoring system for an intelligent power distribution cabinet includes:

[0058] The first image acquisition module is used to acquire instrument data images of the power distribution cabinet;

[0059] The data acquisition module is used to acquire instrument data based on the instrument data image;

[0060] The data judgment module is used to determine whether the instrument data matches the preset normal data;

[0061] The second image acquisition module is used to acquire an infrared image of the power distribution cabinet when the instrument data does not match the normal data.

[0062] An anomaly detection module is used to determine whether an anomaly has occurred inside the power distribution cabinet based on the infrared image.

[0063] The abnormal information output module is used to acquire and output abnormal information from the power distribution cabinet when an abnormality occurs; and to acquire and output abnormal information from the instruments when no abnormality occurs.

[0064] By adopting the above technical solution, the first image acquisition module acquires the instrument data image of the distribution cabinet and sends it to the connected data acquisition module. The data acquisition module acquires the instrument data based on the instrument data image and sends it to the connected data judgment module. The data judgment module judges whether the instrument data matches the preset normal data and sends the matching result to the connected second image acquisition module. When the matching result is that the instrument data does not match the normal data, the second image acquisition module acquires the infrared image of the distribution cabinet and sends it to the connected anomaly judgment module. The anomaly judgment module judges whether an anomaly has occurred inside the distribution cabinet based on the infrared image and sends the obtained judgment result to the connected anomaly information output module. When an anomaly occurs, the anomaly information output module acquires the anomaly information of the distribution cabinet and outputs it; when no anomaly occurs, the anomaly information output module acquires the instrument anomaly information and outputs it. Thus, by switching between the instrument data image and the infrared image, the fault of the distribution cabinet can be confirmed, thereby improving the monitoring effect of the distribution cabinet.

[0065] In summary, this application includes at least one of the following beneficial technical effects:

[0066] 1. By determining whether the instrument data matches normal data, it can identify any anomalies. If there is a mismatch, an infrared image is acquired, and then the internal structure of the distribution cabinet is assessed based on the infrared image. If an anomaly is detected, it is highly likely that the data anomaly is caused by the distribution cabinet itself; in this case, the distribution cabinet anomaly information is acquired and output. If no anomaly is detected, it is highly likely that the anomaly is caused by the instrument itself; in this case, the instrument anomaly information is acquired and output. By switching between instrument data images and infrared images, faults in the distribution cabinet can be confirmed, thereby improving the monitoring effectiveness of the distribution cabinet.

[0067] 2. Determining whether the current working time exceeds the theoretical working time can help determine whether the instrument has exceeded its normal working life, thereby further identifying the cause of instrument failure and improving the accuracy of monitoring and judgment;

[0068] 3. Based on the location of the abnormality and the location of the electrical appliance, the corresponding appliance type is obtained. At this time, the cause of the fault is obtained and output based on the appliance type. It is possible to find the appliance that is abnormal, and thus determine the type of cause of the abnormality based on the appliance that is abnormal. This makes it easier for maintenance personnel to investigate the cause of the fault and improves the convenience of maintenance. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the overall process of a monitoring method for an intelligent power distribution cabinet provided in an embodiment of this application;

[0070] Figure 2This is a flowchart illustrating steps S11 to S15 after step S7 in one embodiment of this application.

[0071] Figure 3 This is a flowchart illustrating steps S21 to S24 in one embodiment of this application;

[0072] Figure 4 This is a flowchart illustrating steps S31 to S33 before step S24 in one embodiment of this application.

[0073] Figure 5 This is a flowchart illustrating steps S41 to S44 after step S6 in one embodiment of this application.

[0074] Figure 6 This is a flowchart illustrating steps S51 to S54 after step S44 in one embodiment of this application.

[0075] Figure 7 This is a detailed flowchart of steps S61 to S64, i.e., step S53, in one embodiment of this application.

[0076] Figure 8 This is a structural block diagram of a monitoring system for an intelligent power distribution cabinet provided in an embodiment of this application.

[0077] Explanation of reference numerals in the attached figures:

[0078] 1. First image acquisition module; 2. Data acquisition module; 3. Data judgment module; 4. Second image acquisition module; 5. Anomaly judgment module; 6. Anomaly information output module. Detailed Implementation

[0079] The following is in conjunction with the appendix Figure 1-8 This application will be described in further detail.

[0080] This application discloses a monitoring method for intelligent power distribution cabinets.

[0081] Reference Figure 1 The monitoring methods for intelligent power distribution cabinets include:

[0082] S1. Obtain the instrument data images of the power distribution cabinet;

[0083] S2. Acquire instrument data based on instrument data images;

[0084] S3. Determine whether the instrument data matches the preset normal data;

[0085] S4. If there is no match, obtain the infrared image of the power distribution cabinet;

[0086] S5. Determine whether any abnormalities have occurred inside the distribution cabinet based on infrared images;

[0087] S6. If an abnormality occurs, obtain and output the abnormality information of the power distribution cabinet;

[0088] S7. If no abnormality occurs, obtain and output the instrument abnormality information.

[0089] Specifically, during the monitoring of the power distribution cabinet, the first step is to acquire images of the instrument data from the cabinet. This can be done by using a high-definition camera to capture images of the instrument doors. Then, image recognition is used to retrieve the instrument data, such as voltage and current values, from the images.

[0090] Next, it is determined whether the instrument data matches the preset normal data. The preset normal data are the data values ​​during the normal operation of the equipment, and the normal data are range values. For example, the voltage value in the instrument data is matched with the voltage value in the normal data. The voltage value in the normal data includes an upper limit and a lower limit. If the voltage value in the instrument data is between the upper limit and the lower limit of the voltage value in the normal data, it means that the match is successful. The matching method for the current value is the same.

[0091] If the instrument data matches the normal data, it proves that the power distribution cabinet is working normally and no operation is required. If the instrument data does not match the normal data, an infrared image of the power distribution cabinet is acquired. The acquisition process is as follows: when the instrument data does not match the normal data, a switching command is received. According to the switching command, the camera's shooting mode is switched from high-definition camera shooting to infrared camera shooting. The infrared camera then images the power distribution cabinet to obtain an infrared image. In other words, when the instrument data matches the normal data, the high-definition camera operates; when the instrument data does not match the normal data, the infrared camera operates.

[0092] Next, the infrared image is used to determine whether there is an abnormality inside the distribution cabinet. Specifically, the temperature information inside the distribution cabinet is obtained from the acquired infrared image. Different color areas in the infrared image represent different temperature values. By judging whether the temperature value matches the pre-stored temperature, it is determined whether there is an abnormality inside the distribution cabinet.

[0093] For example, an infrared image is divided into several regions, each corresponding to a temperature value. This temperature value is then matched against a pre-stored temperature image, which is also divided into the same number of regions as the infrared image, with each region corresponding to a temperature range. The temperature value of a region in the infrared image is matched against the temperature range of the corresponding region in the temperature image. If the temperature value falls within the temperature range, it indicates no anomaly has occurred; otherwise, it indicates an anomaly has occurred.

[0094] If an anomaly occurs inside the distribution cabinet, it indicates that the mismatch between the instrument data and normal data is most likely due to a malfunction in the equipment within the cabinet. Therefore, the anomaly information should be acquired and output. Conversely, if no anomaly occurs inside the cabinet, the mismatch between the instrument data and normal data is most likely due to a malfunction in the instrument itself. In this case, the anomaly information should also be acquired and output. This information can then be sent to maintenance personnel's mobile phones or tablets, providing reminders and facilitating targeted maintenance of the distribution cabinet. Furthermore, by switching between instrument data images and infrared images, faults in the distribution cabinet can be confirmed, thereby improving the monitoring effectiveness.

[0095] Reference Figure 2 Furthermore, to improve the accuracy of anomaly detection, in another embodiment, after step S7 (if no anomaly occurs, the instrument anomaly information is obtained and output) the following steps are also included:

[0096] S11. Obtain the theoretical working time for the instrument to function normally;

[0097] S12. Obtain the current operating time of the instrument;

[0098] S13. Determine whether the current working time exceeds the theoretical working time;

[0099] S14. If the error exceeds the limit, the output instrument abnormality information indicates normal damage.

[0100] S15. If the error is not exceeded, the output instrument abnormality information will be abnormal damage.

[0101] Specifically, when no abnormalities occur inside the distribution cabinet, the system first retrieves the theoretical operating time of the instruments. This theoretical operating time refers to the duration the instruments can function normally after leaving the factory; it can be understood as the shelf life of food. This theoretical operating time is the instrument's shelf life; exceeding this time and subsequent damage is considered normal. This data is pre-stored in the system based on the instrument's purchase information, and the retrieval process is the same as the fetching process.

[0102] Next, obtain the current working duration of the instrument. This can be done by recording the time the instrument was powered on and working, then recording the current time value, and subtracting the current time value from the instrument's power-on working time value. The resulting value is the current working duration.

[0103] Next, it determines whether the current working time exceeds the theoretical working time, thus indicating whether the instrument has exceeded its normal working life. If it does, it suggests that the current instrument damage is likely due to normal circumstances, and the output instrument abnormality information is normal damage, reminding maintenance personnel that the instrument needs to be replaced. If it does not exceed the limit, it suggests that the current instrument damage is likely caused by other external factors, and the output instrument abnormality information is abnormal damage, thus reminding staff to investigate the cause of the instrument damage.

[0104] The above methods can further determine whether the instrument malfunction is due to normal or abnormal damage, thereby improving the accuracy of the judgment and monitoring, enhancing the monitoring effect, reducing the number of times staff need to investigate the cause, and further improving the efficiency of staff in maintenance.

[0105] Reference Figure 3 Furthermore, to further confirm the abnormal damage, in another embodiment, the abnormal damage information of the output instrument includes the following steps:

[0106] S21. Obtain the vibration parameters of the distribution cabinet;

[0107] S22. Determine whether the vibration parameters are greater than the parameter threshold;

[0108] S23. If so, the abnormal damage output is vibration damage;

[0109] S24. If not, the output of abnormal corruption is self-corruption.

[0110] Abnormal damage includes vibration damage and self-damage; specifically, when the current working time exceeds the theoretical working time, the vibration parameters of the distribution cabinet are obtained. The vibration parameters include vibration duration and vibration frequency. The vibration frequency can be obtained by measuring with a vibration sensor, and the vibration duration can be measured by a timer.

[0111] Then, it is determined whether the vibration parameters are greater than the parameter thresholds, including frequency thresholds and duration thresholds. This can be done by checking if the vibration frequency is greater than the frequency threshold; if so, the vibration parameter is greater than the threshold, and vice versa. Alternatively, it can be determined if the vibration duration is greater than the duration threshold; if so, the vibration parameter is greater than the threshold, and vice versa. The specific determination method can be set according to the actual situation.

[0112] If the vibration parameter is greater than the parameter threshold, it proves that the instrument damage is most likely caused by vibration. At this time, the abnormal output damage is confirmed as vibration damage, which can alert the staff and help them investigate the cause of the vibration.

[0113] If the vibration parameter is less than or equal to the parameter threshold, it proves that the instrument damage is most likely not caused by vibration. In this case, the abnormal output damage is confirmed as self-damage, which allows the staff to inspect the instrument itself in order to find the problem.

[0114] By using the methods described above, the cause of instrument damage can be further clarified, which can then remind staff to better understand the cause of the malfunction, further reducing the difficulty of troubleshooting and improving the convenience and efficiency of maintenance.

[0115] Reference Figure 4 Furthermore, in another embodiment, before step S24, i.e., before the output of abnormal damage as self-damage, the following steps are also included:

[0116] S31. Obtain the current humidity value inside the power distribution cabinet;

[0117] S32. Determine whether the current humidity value is greater than the humidity threshold and obtain the first determination result;

[0118] S33. If the first judgment result is yes, then output a message indicating that the product is damaged due to moisture.

[0119] Specifically, the humidity value inside the distribution cabinet is measured by a humidity detector, i.e., the current humidity value. Then, it is determined whether the current humidity value is greater than the humidity threshold, and the first judgment result is obtained. This allows it to determine whether the current humidity value inside the distribution cabinet will damage the normal operation of the instrument.

[0120] The humidity threshold is the maximum humidity value required for the instrument to function normally. If the humidity value exceeds the humidity threshold, it will affect the normal operation of the instrument or even damage it.

[0121] If the first judgment result is yes, it proves that the current humidity value in the distribution cabinet is too high, which will affect the normal use of the instrument. It also indicates that the current instrument damage is most likely caused by the excessive humidity value. At this time, the output moisture damage warning can remind the staff that the instrument damage may also be caused by the excessive humidity in the distribution cabinet, thus providing the staff with a direction for inspection and facilitating the maintenance personnel to troubleshoot in a timely manner.

[0122] If the first judgment result is negative, then step S24 is executed, that is, the abnormal damage output is self-damage, thereby eliminating the factor of humidity, reducing interference factors, and further improving the accuracy of judgment.

[0123] Furthermore, in another embodiment, when the first determination result is yes, after outputting the moisture damage prompt, in order to reduce the humidity in the power distribution cabinet and ensure the normal operation of other equipment in the power distribution cabinet as much as possible, a heating command is obtained to make the current humidity value less than or equal to the humidity threshold.

[0124] Specifically, this can be achieved by obtaining a heating command and controlling the heater inside the distribution cabinet to heat the system, thereby reducing the current humidity to less than or equal to the humidity threshold. This ensures the dryness of the distribution cabinet as much as possible, reduces the possibility of short circuits, and improves the safety of the distribution cabinet during operation.

[0125] Reference Figure 5 The power distribution cabinet contains several electronic components. Simply identifying an abnormal temperature inside the cabinet requires workers to check each component individually, which is quite cumbersome. Therefore, to reduce the complexity of the operation, in another embodiment, after step S6 (if an abnormality occurs, obtaining and outputting the power distribution cabinet abnormality information) the following steps are also included:

[0126] S41. Identify abnormal locations based on infrared images;

[0127] S42. Obtain the location of electrical appliances inside the distribution cabinet;

[0128] S43. Obtain the corresponding appliance type based on the abnormal location and appliance location;

[0129] S44. Obtain and output fault cause information based on appliance type.

[0130] Specifically, the abnormal locations are first identified based on infrared images, specifically areas with abnormal temperatures. Then, the locations of electrical appliances within the distribution cabinet are determined; these locations are fixed installation positions and are stored in advance.

[0131] The location of electrical appliances can be stored by pre-storing location images. These images define installation areas for various appliances, each corresponding to a specific appliance. The location images and infrared images can be matched based on their aspect ratios. Therefore, once an anomaly is detected, the location of the corresponding appliance can be retrieved from the location images, thus identifying the appliance's location.

[0132] Then, based on the abnormal location and the electrical location, the corresponding electrical type is obtained. That is, after obtaining the electrical location corresponding to the abnormal location, the type of electrical equipment at the corresponding location can be obtained, i.e., the electrical type. The electrical type can be capacitor, transformer, miniature circuit breaker, etc.

[0133] Finally, fault cause information can be obtained and output based on the appliance type. Specifically, the specific model of the malfunctioning appliance can be determined by its type. Then, based on pre-stored fault causes, the specific model can be matched with the stored appliance data to identify the possible cause of the fault. This information is then transmitted to the terminal device via wired or wireless communication to alert personnel. This facilitates troubleshooting for maintenance personnel and improves the convenience of repair.

[0134] Reference Figure 6 Furthermore, in the aforementioned determination of the causes of power distribution cabinet failures, all were due to abnormal temperature. Therefore, if the heat dissipation of the power distribution cabinet is abnormal, it will also lead to abnormal temperature of the equipment inside the cabinet. Therefore, to reduce the impact of heat dissipation on the judgment of abnormal temperature, in another embodiment, after step S44 (i.e., obtaining and outputting the fault cause information based on the electrical appliance type), the following steps are also included:

[0135] S51. Obtain the fan status of the cooling fan in the power distribution cabinet;

[0136] S52. Determine whether the cooling fan is functioning normally based on its status, and obtain a second determination result;

[0137] S53. If the second judgment result is yes, then the output fault cause information is the equipment itself fault;

[0138] S54. If the second judgment result is negative, the output fault cause information is cooling fan failure.

[0139] Specifically, the first step is to obtain the fan status of the cooling fans inside the distribution cabinet. Fan status refers to the fan's rotation speed and its integrity. The fan's rotation speed can be obtained through a speed sensor, and the fan's integrity can be obtained through camera footage.

[0140] Then, the system determines whether the cooling fan is functioning normally based on the fan's status, obtaining a second judgment result, i.e., whether the cooling fan is working properly. This judgment can be made by comparing the rotation speed with a pre-stored speed threshold; if they are not equal, the cooling fan is abnormal, i.e., the second judgment result is negative. Simultaneously, the system uses an image recognition algorithm to determine if the fan blades are intact, based on an image captured by a camera. If they are not intact, the cooling fan is abnormal, i.e., the second judgment result is negative. Only when the rotation speed equals the speed threshold and the cooling fan is intact is the cooling fan functioning normally, i.e., the second judgment result is positive.

[0141] When the second judgment result is negative, it proves that the abnormal temperature of the electrical equipment may be caused by abnormal heat dissipation of the cooling fan. At this time, the fault cause information is output as cooling fan failure, thus reminding the staff to first troubleshoot the cooling fan failure.

[0142] If the second judgment result is yes, it proves that the abnormal temperature of the electrical equipment is most likely caused by a malfunction in the equipment itself. Therefore, the output cause information is a fault in the equipment itself, thus reminding the staff to directly inspect the electrical equipment itself without needing to repair the cooling fan. This further improves the accuracy of equipment cause judgment and reduces the time wasted during the repair process.

[0143] Reference Figure 7 When the cooling fan is working normally, a blocked vent can also cause abnormal heat dissipation. Therefore, in another embodiment, step S53, i.e., if the second judgment result is yes, then the output fault cause information is a device fault, includes the following steps:

[0144] S61. Obtain an image of the heat dissipation vents of the power distribution cabinet;

[0145] S62. Determine whether the heat dissipation vents of the power distribution cabinet are normal based on the heat dissipation vent image;

[0146] S63. If not, the output fault cause information is heat dissipation port failure;

[0147] S64. If so, the output fault cause information is a fault in the device itself.

[0148] Specifically, when the second judgment result is yes, the heat dissipation vent image of the power distribution cabinet is obtained. The acquisition method can be by taking a picture with a camera. Then, the heat dissipation vent image is used to determine whether the heat dissipation vent of the power distribution cabinet is normal. The judgment method can be to compare the heat dissipation vent image with a pre-stored normal heat dissipation vent image to determine whether the two match. If they do not match, it proves that the heat dissipation vent of the power distribution cabinet is likely to be blocked or other abnormal, that is, the heat dissipation vent is abnormal.

[0149] If not, it means the heat dissipation vent of the distribution cabinet is abnormal. This indicates that the abnormal temperature may be caused by a malfunction of the heat dissipation vent. In this case, the fault cause information will be output as heat dissipation vent malfunction, which will make it convenient for staff to inspect and repair the heat dissipation vent in a timely manner.

[0150] If the heat dissipation vents of the distribution cabinet are normal, it proves that the temperature abnormality is not caused by heat dissipation, thus further ruling out the possibility of external factors causing the temperature abnormality. In this case, it is highly likely that the temperature abnormality is caused by a problem with the electrical equipment itself, and therefore the output fault cause information is a fault in the equipment itself. Through the above method, the cause of the fault can be output more specifically, further improving the monitoring effect and making it easier for maintenance personnel to perform repairs.

[0151] The implementation principle of a monitoring method for an intelligent power distribution cabinet according to an embodiment of this application is as follows: Instrument data is acquired based on the instrument data image, and it is determined whether the instrument data matches normal data to identify any abnormalities. If there is a mismatch, an abnormality is detected. Infrared images are then acquired, and the internal structure of the power distribution cabinet is assessed based on these images to determine if the abnormal instrument data is caused by an internal fault. If an abnormality occurs, it is highly likely that the data abnormality is caused by an internal fault within the power distribution cabinet. In this case, the abnormality information of the power distribution cabinet is acquired and output. If no abnormality occurs, it is highly likely that the abnormality is caused by the instrument itself. In this case, the abnormality information of the instrument is acquired and output. By switching between instrument data images and infrared images, faults in the power distribution cabinet can be confirmed, thereby improving the monitoring effect of the power distribution cabinet.

[0152] This application also discloses a monitoring system for an intelligent power distribution cabinet, which can achieve the same technical effect as the monitoring method for an intelligent power distribution cabinet described above.

[0153] Reference Figure 8 The monitoring system for the intelligent power distribution cabinet includes:

[0154] The first image acquisition module 1 is used to acquire instrument data images of the power distribution cabinet;

[0155] Data acquisition module 2 is used to acquire instrument data based on instrument data images;

[0156] Data judgment module 3 is used to determine whether the instrument data matches the preset normal data;

[0157] The second image acquisition module 4 is used to acquire infrared images of the power distribution cabinet when the instrument data does not match the normal data.

[0158] Anomaly detection module 5 is used to determine whether an anomaly has occurred inside the power distribution cabinet based on infrared images;

[0159] The abnormal information output module 6 is used to acquire and output abnormal information of the power distribution cabinet when an abnormality occurs; and to acquire and output abnormal information of the instrument when no abnormality occurs.

[0160] Specifically, after the first image acquisition module 1 acquires the instrument data image of the power distribution cabinet, it sends it to the data acquisition module 2 connected to it. The data acquisition module 2 acquires the instrument data based on the instrument data image and sends it to the data judgment module 3 connected to it.

[0161] The data judgment module 3 determines whether the instrument data matches the preset normal data and sends the matching result to the second image acquisition module 4 connected to it. When the matching result is that the instrument data does not match the normal data, the second image acquisition module 4 acquires the infrared image of the power distribution cabinet and sends it to the anomaly judgment module 5 connected to it.

[0162] The anomaly detection module 5 determines whether an anomaly has occurred inside the distribution cabinet based on the infrared image and sends the result to the connected anomaly information output module 6. When an anomaly occurs, the anomaly information output module 6 acquires and outputs the anomaly information of the distribution cabinet; when no anomaly occurs, the anomaly information output module 6 acquires and outputs the instrument anomaly information.

[0163] Furthermore, by switching between instrument data images and infrared images, faults in the power distribution cabinet can be confirmed, thereby improving the monitoring effect of the power distribution cabinet.

[0164] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A monitoring method of an intelligent power distribution cabinet, characterized in that, The method comprises the following steps: acquiring an instrument data image of a power distribution cabinet; acquiring instrument data based on the instrument data image; determining whether the instrument data matches preset normal data; if not, acquiring an infrared image of the power distribution cabinet; determining whether an abnormality occurs inside the power distribution cabinet based on the infrared image; if an abnormality occurs, acquiring power distribution cabinet abnormality information and outputting the information; if no abnormality occurs, determining that an instrument abnormality exists, acquiring instrument abnormality information and outputting the information; wherein, after acquiring the instrument abnormality information and outputting the information, the following steps are further included: acquiring a theoretical working duration during which the instrument can work normally; acquiring a current working duration during which the instrument works; determining whether the current working duration exceeds the theoretical working duration; if yes, outputting the instrument abnormality information as normal damage; if no, outputting the instrument abnormality information as abnormal damage; wherein, the outputting of the instrument abnormality information as abnormal damage comprises the following steps: acquiring a vibration parameter of the power distribution cabinet; determining whether the vibration parameter is greater than a parameter threshold value; if yes, outputting the abnormal damage as vibration damage; if no, outputting the abnormal damage as self damage; wherein, before the outputting of the abnormal damage as self damage, the following steps are further included: acquiring a current humidity value in the power distribution cabinet; determining whether the current humidity value is greater than a humidity threshold value and obtaining a first determination result; if the first determination result is yes, outputting a damp damage prompt; if the first determination result is no, proceeding to the next step; wherein, after the outputting of the damp damage prompt, the following steps are further included: acquiring a heating instruction, and controlling a heater in the power distribution cabinet to heat through the heating instruction, so that the current humidity value is less than or equal to the humidity threshold value.

2. The monitoring method according to claim 1, characterized in that, after the acquiring of the power distribution cabinet abnormality information and the outputting of the information, the following steps are further included: acquiring an abnormal position based on the infrared image; acquiring an electrical appliance position in the power distribution cabinet; acquiring a corresponding electrical appliance type based on the abnormal position and the electrical appliance position; acquiring fault cause information based on the electrical appliance type and outputting the information.

3. The monitoring method according to claim 2, characterized in that, after the acquiring of the fault cause information based on the electrical appliance type and the outputting of the information, the following steps are further included: acquiring a fan state of a heat dissipation fan in the power distribution cabinet; determining whether the state of the heat dissipation fan is normal based on the fan state and obtaining a second determination result; if the second determination result is yes, outputting the fault cause information as a device self fault; if the second determination result is no, outputting the fault cause information as a heat dissipation fan fault.

4. The monitoring method according to claim 3, characterized in that, the outputting of the fault cause information as a device self fault comprises the following steps: acquiring a heat dissipation port image of the power distribution cabinet; determining whether a heat dissipation port of the power distribution cabinet is normal based on the heat dissipation port image; if no, outputting the fault cause information as a heat dissipation port fault; if yes, outputting the fault cause information as the device self fault.

5. A monitoring system of an intelligent power distribution cabinet, characterized in that, The method comprises the following steps: a first image acquisition module (1) is configured to acquire an instrument data image of a power distribution cabinet; a data acquisition module (2) is configured to acquire instrument data based on the instrument data image; a data determination module (3) is configured to determine whether the instrument data matches preset normal data; The second image acquisition module (4) is configured to acquire an infrared image of the power distribution cabinet when the meter data does not match the normal data; The abnormality judgment module (5) is configured to judge whether an abnormality occurs inside the power distribution cabinet based on the infrared image; The abnormality information output module (6) is configured to acquire and output power distribution cabinet abnormality information when an abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (6) is configured to acquire and output meter abnormality information when no abnormality occurs; The abnormality information output module (

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