Intelligent explosion-proof power distribution device with integrated wireless transmission function

Abstract

The present application relates to the technical field of explosion-proof power distribution devices, and specifically to an intelligent explosion-proof power distribution device with integrated wireless transmission function, the device comprising: a data acquisition module for obtaining each operating parameter of the explosion-proof power distribution device at each moment in each acquisition cycle; a communication transmission module for transmitting each operating parameter acquired by the data acquisition module to an intelligent monitoring module through a signal transmitter; an intelligent monitoring module for evaluating whether there is a risk of failure in the explosion-proof power distribution device, including: determining the relative difference in the state of each acquisition cycle; determining the intra-cluster fluctuation amount of each clustering cluster corresponding to each acquisition cycle, determining the parameter fluctuation degree of each acquisition cycle; determining the state abnormality of each acquisition cycle; determining the potential failure coefficient of each acquisition cycle, determining the explosion-proof warning value of each acquisition cycle, and evaluating the failure risk of the explosion-proof power distribution device. The present application can improve the accuracy of the diagnosis of the failure risk of the explosion-proof power distribution device.

Description

Technical Field

[0001] The present application relates to the technical field of explosion-proof power distribution devices, and in particular to an intelligent explosion-proof power distribution device with integrated wireless transmission function. Background Art

[0002] Explosion-proof power distribution device is a safety device used in flammable and explosive environments. Its main function is to prevent electrical sparks from causing explosion accidents. Such devices are widely used in petrochemical, coal mining, hazardous chemical production and other fields. With the improvement of industrial safety standards and the advancement of explosion-proof technology, the design of explosion-proof power distribution devices pays more attention to intrinsic safety and intelligent monitoring to improve safety performance in flammable and explosive environments and reduce the risk of accidents.

[0003] Usually, explosion-proof power distribution devices are located in harsh environments with large load changes. With long-term operation, there are some potential faults, such as aging or loosening of the isolation contacts, which leads to increased contact resistance and heat generation. Existing intelligent explosion-proof power distribution devices mainly focus on the protection of the power grid, and often use selective leakage protection methods based on zero-sequence current and first half-wave principles, and their detection methods are relatively simple. Affected by environmental changes such as temperature and humidity, dust changes, and voltage and power disturbances, there is a defect of low accuracy in explosion-proof fault risk diagnosis. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent explosion-proof power distribution device with integrated wireless transmission function is provided to solve the existing problems.

[0005] The solution to the technical problem of the present application is to provide an intelligent explosion-proof power distribution device with integrated wireless transmission function, the device comprising:

[0006] The data acquisition module is used to obtain each operating parameter of the explosion-proof power distribution device at each moment in each acquisition cycle;

[0007] A communication transmission module, used to transmit each operating parameter collected by the data collection module to the intelligent monitoring module through a signal transmitter;

[0008] Intelligent monitoring module to assess whether there is a risk of failure in explosion-proof power distribution devices, including:

[0009] (1) Clustering the operating parameters at all times in each acquisition cycle to obtain the local density and relative distance corresponding to each time, as well as multiple clusters; determining the relative difference of the state of each acquisition cycle based on the local density and the relative distance;

[0010] (2) Determine the intra-cluster fluctuation of each cluster corresponding to each acquisition cycle according to the changes of each operating parameter in each cluster corresponding to each acquisition cycle at different times, analyze the discrete distribution of all acquisition moments in different clusters, and determine the parameter fluctuation of each acquisition cycle in combination with the intra-cluster fluctuation; determine the state abnormality of each acquisition cycle based on the relative difference of the state and the parameter fluctuation;

[0011] (3) Analyze the difference between the state abnormality degree of each collection cycle and the other collection cycles, determine the potential failure coefficient of each collection cycle, determine the explosion-proof warning value of each collection cycle based on the potential failure coefficient, and evaluate the failure risk of the explosion-proof power distribution device.

[0012] Preferably, each of the operating parameters includes voltage, power, temperature, humidity and dust concentration.

[0013] Preferably, the local density and relative distance corresponding to each moment, and the method for obtaining multiple clusters are:

[0014] All the operating parameters at each moment in each acquisition cycle are combined into a parameter feature vector;

[0015] Cluster analysis is performed on the parameter feature vectors at all moments in each acquisition cycle to obtain the local density and relative distance corresponding to each moment, as well as multiple clusters.

[0016] Preferably, determining the relative difference of the state of each acquisition cycle includes:

[0017] The ratio of the relative distance to the local density is calculated and recorded as a first ratio, and the average of the first ratio at all times in each acquisition cycle is taken as the relative difference of the state of each acquisition cycle.

[0018] Preferably, the determining of the intra-cluster fluctuation amount of each cluster corresponding to each acquisition period includes:

[0019] Calculate the autocorrelation coefficient of each operating parameter in each cluster at all times;

[0020] The average of the absolute values ​​of the autocorrelation coefficients of all the operating parameters in each cluster is used as the intra-cluster fluctuation amount of each cluster corresponding to each acquisition period.

[0021] Preferably, determining the parameter fluctuation of each acquisition cycle includes:

[0022] Calculate the discrete degree of the time corresponding to all the parameter feature vectors in each cluster;

[0023] The ratio of the dispersion degree to the intra-cluster fluctuation is calculated and recorded as a second ratio. The average of the second ratios of all clusters corresponding to each acquisition period is taken as the parameter fluctuation of each acquisition period.

[0024] Preferably, the state abnormality degree of each acquisition cycle is the product of the state relative difference and the parameter fluctuation degree.

[0025] Preferably, determining the potential failure coefficient of each acquisition cycle includes:

[0026] Select multiple acquisition cycles during the initial operation of the explosion-proof power distribution device and record them as reference cycles;

[0027] The difference in the state abnormality between each acquisition cycle and any reference cycle is calculated and recorded as the abnormality difference; the sum of the abnormality differences between each acquisition cycle and all reference cycles is taken as the potential failure coefficient of each acquisition cycle.

[0028] Preferably, the explosion-proof warning value of each acquisition cycle is determined by calculating: ,in, For the The explosion-proof warning value of a collection cycle, For the The potential failure factor of each acquisition cycle is is the logarithmic function with base 10.

[0029] Preferably, the evaluating the failure risk of the explosion-proof power distribution device includes: if the explosion-proof warning value is greater than a preset threshold, there is a failure risk of the explosion-proof power distribution device; otherwise, the explosion-proof power distribution device operates normally.

[0030] This application has at least the following beneficial effects:

[0031] The present application clusters the operating parameters at all times in each acquisition cycle to obtain the local density and relative distance corresponding to each time, as well as multiple clustering clusters; based on the local density and the relative distance, the relative difference of the state of each acquisition cycle is determined, and its beneficial effect lies in that the distribution density of the data of all operating parameters of the explosion-proof power distribution device at different times is taken into account to reflect the changes in the operating parameters at different times, and then reflect the difference in the changes in the operating parameters in the entire acquisition cycle; according to the relevant changes of each operating parameter in each clustering cluster corresponding to each acquisition cycle at different times, the intra-cluster fluctuation amount of each clustering cluster corresponding to each acquisition cycle is determined, the discrete distribution of all acquisition moments in different clustering clusters is analyzed, and the parameter fluctuation degree of each acquisition cycle is determined in combination with the intra-cluster fluctuation amount; based on the relative state The difference and the parameter fluctuation degree are used to determine the state abnormality of each acquisition cycle. The beneficial effect is that the discrete changes of each operating parameter in different clustering clusters are taken into account to reflect the frequency of random fluctuations of all operating parameters in the corresponding clustering clusters, and then the state stability of the explosion-proof distribution device is explained; the difference degree of the state abnormality between each acquisition cycle and the other acquisition cycles is analyzed to determine the potential fault coefficient of each acquisition cycle, and the explosion-proof warning value of each acquisition cycle is determined based on the potential fault coefficient, and the failure risk of the explosion-proof distribution device is evaluated. The beneficial effect is that the state of each acquisition cycle is compared with the state of the acquisition cycle of the explosion-proof distribution device during initial operation, which can accurately evaluate the potential failure risk of the explosion-proof distribution device and improve the accuracy of the fault risk diagnosis of the explosion-proof distribution device. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The intelligent explosion-proof power distribution device with integrated wireless transmission function of the present application is further described in detail below with reference to the accompanying drawings.

[0033] Figure 1 A block diagram of an intelligent explosion-proof power distribution device with integrated wireless transmission function provided by one embodiment of the present application;

[0034] Figure 2 A schematic diagram of an explosion-proof power distribution device provided in one embodiment of the present application;

[0035] Figure 3 A flowchart of the steps of an intelligent monitoring module provided by one embodiment of the present application;

[0036] Figure 4 A flowchart of the steps of a method for obtaining the relative difference of states in each acquisition cycle provided by an embodiment of the present application;

[0037] Figure 5 A flowchart of the steps of a method for obtaining the state abnormality degree of each acquisition cycle provided by an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the following is a further detailed description of the intelligent explosion-proof power distribution device with integrated wireless transmission function proposed in the present application in combination with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0039] Unless defined otherwise, all 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.

[0040] See also Figure 1 , which shows a block diagram of an intelligent explosion-proof power distribution device with integrated wireless transmission function provided by an embodiment of the present application. The device includes: a data acquisition module, a communication transmission module, and an intelligent monitoring module.

[0041] The data acquisition module is used to obtain each operating parameter of the explosion-proof power distribution device at each moment in each acquisition cycle, and each operating parameter includes voltage, power, temperature, humidity and dust concentration.

[0042] The explosion-proof power distribution device includes a power distribution cabinet body, which adopts an aluminum alloy die-cast shell or a stainless steel and carbon steel welded shell, and adopts a flameproof or composite explosion-proof structure. The power distribution cabinet body has a cavity, and a molded case circuit breaker, a temperature sensor, a humidity sensor, a voltage sensor, a power sensor and a gas concentration sensor are arranged in the cavity. A signal transmitter is arranged on the top of the power distribution cabinet body.

[0043] An LED lighting lamp is fixed in the power distribution cabinet body. The molded case circuit breaker can be closed / opened by remote signal or by the manual handle of the box cover, and is provided with a closing / opening state signal indicator. The molded case circuit breaker is provided with over-voltage, under-voltage, over-current, phase loss, unbalance protection, leakage protection and alarm functions.

[0044] The power distribution cabinet body is assembled from a shell and a shell cover, a protective sealing strip or a sealing gasket is arranged between the shell and the shell cover, and a rain cover is detachably installed on the top of the shell.

[0045] A ventilation fan is installed on the side wall of the housing, and a shutter is installed on the side of the ventilation fan facing the outside, the shutter prevents rain and steam from outside from invading the cavity. The ventilation fan can achieve smooth airflow inside and outside the power distribution device.

[0046] A controller is installed on the other side wall of the power distribution cabinet body opposite to the ventilation fan. The controller is electrically connected to all the above sensors and transmits the monitoring data of all sensors to the user through the signal transmitter. The controller is equipped with a 485 communication interface 4G signal control. The power distribution cabinet body has a locator, and multiple power distribution cabinets are networked and controlled. When a fault alarm occurs in the distribution box, rapid positioning and maintenance can be achieved.

[0047] A display screen is installed on the shell cover, which can be used to set and display electrical parameters.

[0048] Further, the schematic diagram of the explosion-proof power distribution device provided in this embodiment is as follows Figure 2 As shown, 1 is the main body of the distribution device, 2 is the display screen, 3 is the closing / opening status signal indicator light, and 4 is the ventilation fan.

[0049] Excessively high humidity inside the distribution cabinet may affect the insulation performance of the air, resulting in a reduction in the insulation resistance of electrical equipment and causing breakdown, while excessively high temperature inside the distribution cabinet may cause equipment overheating, accelerated aging, or even burn out the equipment; secondly, dust in the air may accumulate between the switch contacts, resulting in poor contact and accelerated wear of the equipment. In certain environments, excessive dust concentration may even cause a dust explosion.

[0050] In order to monitor the working status of the explosion-proof distribution device in real time, the voltage, power, temperature and humidity of the distribution cabinet when it is working are collected in real time through the voltage sensor, power sensor, temperature sensor and humidity sensor installed in the cavity of the distribution cabinet body; the dust concentration in the air around the distribution cabinet is collected through the dust concentration detector; the collection time interval is t, the duration of each collection is T, and each operating parameter at each moment in each collection cycle is obtained, and each operating parameter includes voltage, power, temperature, humidity and dust concentration.

[0051] Preferably, in this embodiment, the collection time interval is 0.1s, and each collection cycle is 10s. As other implementation methods, the implementer can set them according to actual conditions.

[0052] So far, each operating parameter of the explosion-proof power distribution device at each moment in each collection cycle is obtained, and each operating parameter includes voltage, power, temperature, humidity and dust concentration.

[0053] The communication transmission module is used to transmit each operating parameter collected by the data acquisition module to the intelligent monitoring module through the signal transmitter.

[0054] The signal transmitter is installed on the top of the power distribution cabinet to transmit the data collected in the data acquisition module to the intelligent monitoring module for analysis and real-time monitoring. The signal transmitter of the explosion-proof power distribution device integrates M2M wireless communication technology to improve the real-time and reliability of communication transmission while reducing strong electromagnetic interference to communication.

[0055] Intelligent monitoring module for evaluating whether there is a risk of failure in explosion-proof power distribution equipment.

[0056] Further, the step flow chart of the intelligent monitoring module provided in this application is as follows Figure 3 shown.

[0057] Step 1: cluster the operating parameters at all times in each acquisition cycle to obtain the local density and relative distance corresponding to each time, as well as multiple clustering clusters; based on the local density and the relative distance, determine the relative difference of the state of each acquisition cycle.

[0058] In the intelligent monitoring module, the collected data is analyzed and processed to provide early warnings of potential faults and safety hazards.

[0059] First, the voltage, power, temperature, humidity and dust concentration at each moment in each acquisition cycle are preprocessed, including missing value filling and normalization. In this embodiment, linear interpolation is used to fill missing values, and maximum and minimum value normalization is used for normalization. As other implementation methods, implementers can adopt other methods of the prior art, such as cubic spline interpolation filling, Z-score normalization processing method, etc. This embodiment does not impose any special restrictions on this.

[0060] When the explosion-proof power distribution cabinet equipment is in good operating condition, the changes of various data are relatively slow. If there is a sudden increase in some data, it is easy to cause safety accidents. For example, under high-load operation, the rapid increase in voltage may cause arcs and sparks, resulting in damage to the control system and motor-related equipment in the power distribution cabinet. There will also be mutual influences between different data. For example, the increase in voltage and power will cause the electrical equipment in the explosion-proof power distribution device to overheat, causing the temperature to increase rapidly, thus causing a fire.

[0061] Based on the above analysis, the electrical parameters (voltage, power) and environmental parameters (temperature, humidity, dust concentration) in the power distribution device are integrated and analyzed, and the data of all operating parameters are clustered to reflect the distribution of the data, specifically:

[0062] All the operating parameters at each moment in each acquisition cycle are combined into a parameter feature vector;

[0063] It should be noted that if the operating state or the environment of the power distribution equipment changes rapidly at certain moments, the difference between the 5-dimensional parameter feature vectors corresponding to these moments and other moments will become larger.

[0064] Using a density peak clustering algorithm, cluster analysis is performed on the parameter feature vectors at all times in each acquisition cycle to obtain the local density and relative distance corresponding to the parameter feature vectors at each time in each acquisition cycle, as well as multiple cluster clusters;

[0065] It should be noted that the density peak clustering algorithm is a well-known technology and will not be described in detail here. When the cutoff distance is set in the density peak clustering algorithm, the number of sample points that satisfy the average distance around each sample point less than the cutoff distance accounts for 2% of the number of all sample points in the data set.

[0066] It should be noted that the local density reflects the concentration of the corresponding sample points in the local range. The smaller the local density, the fewer other moments in the operation of the explosion-proof power distribution device that are close to the operating state at that moment. The relative distance reflects the minimum distance between the sample point at the corresponding moment and other sample points with higher density. The larger the relative distance, the more isolated the sample point at that moment is relative to other sample points with higher density during the operation of the explosion-proof power distribution device, that is, some data in the parameter feature vector corresponding to the sample point at that moment may have large changes.

[0067] Further, based on the local density and the relative distance, the relative difference of the state is determined to reflect the difference degree of the operating state of the explosion-proof power distribution device at different times in each collection cycle, specifically:

[0068] Calculate the ratio of the relative distance to the local density, record it as a first ratio, and take the average of the first ratio at all times in each acquisition period as the relative difference of the state of each acquisition period;

[0069] Preferably, in this embodiment, the calculation formula for the relative difference of the state at each moment in each acquisition cycle is: ,in For the The relative difference of the states in the acquisition cycle, For the In the collection cycle The relative distance corresponding to the parameter feature vector at the moment, For the In the collection cycle The local density corresponding to the parameter eigenvector at time, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment, The value is 0.01. As other implementation methods, the implementer can set it according to the actual situation. is the first ratio.

[0070] It should be noted that the larger the first ratio is, the greater the difference between the operating status of the explosion-proof power distribution device at the corresponding moment and the operating status at other moments. The larger the relative difference in the status is, the greater the difference in the operating status of the explosion-proof power distribution device at different moments in the corresponding collection period is, and the greater the possibility of abnormality in the operating status of the explosion-proof power distribution device is, and the higher the risk of failure is.

[0071] Furthermore, the step flow chart of the method for obtaining the relative difference of the state of each acquisition cycle provided by the present application is as follows: Figure 4 shown.

[0072] At this point, the relative difference in the state of each acquisition cycle is obtained.

[0073] Step 2: Determine the intra-cluster fluctuation of each cluster corresponding to each acquisition cycle according to the changes of each operating parameter in each cluster corresponding to each acquisition cycle at different times, analyze the discrete distribution of all acquisition moments in different clusters, and determine the parameter fluctuation of each acquisition cycle in combination with the intra-cluster fluctuation; determine the state abnormality of each acquisition cycle based on the relative difference in the state and the parameter fluctuation.

[0074] Furthermore, each cluster represents a collection of moments with similar operating states of an explosion-proof power distribution device. If the environment changes or the disturbances of voltage and power are more frequent, the explosion-proof power distribution device is more likely to have safety hazards caused by arc sparks and high temperature. At this time, the worse the time continuity of all moments in each cluster, the more frequent the random fluctuations of the data in the cluster. Therefore, the discrete degree of all moments in each cluster is analyzed, specifically:

[0075] Calculate the discrete degree of the time corresponding to all the parameter feature vectors in each cluster;

[0076] Preferably, in this embodiment, the degree of discreteness is calculated by calculating the standard deviation of the moments corresponding to all the parameter feature vectors in each cluster. As other implementation methods, the implementer can adopt other methods of the prior art, such as variance, coefficient of variation, etc., and this embodiment does not impose any special restrictions on this.

[0077] It should be noted that the greater the degree of discreteness, the more frequent the changes in the environment or the disturbances in voltage and power of the explosion-proof power distribution device.

[0078] Furthermore, when the explosion-proof power distribution device has an efficiency change or overload, power grid fluctuation, or poor connection of electrical equipment, a certain degree of random change will still exist in the data of the same cluster. For example, the power data at all times in the same cluster may fluctuate randomly. Therefore, the change of each operating parameter in the cluster is analyzed to determine the fluctuation amount within the cluster, which is as follows:

[0079] Calculate the autocorrelation coefficient of each operating parameter in each cluster at all times;

[0080] Preferably, in this embodiment, the time delay of the autocorrelation coefficient is set to 2, wherein the calculation of the autocorrelation coefficient is a well-known technique and will not be described in detail here. As other implementation methods, the implementer can set it according to the actual situation.

[0081] The average of the absolute values ​​of the autocorrelation coefficients of all the operating parameters in each cluster is used as the intra-cluster fluctuation amount of each cluster corresponding to each acquisition period.

[0082] It should be noted that the calculation of the autocorrelation coefficient is a well-known technology and will not be repeated here. The autocorrelation coefficient reflects the correlation between data at different times. The larger the absolute value of the autocorrelation coefficient, the stronger the correlation. Since the operating status of the equipment in the explosion-proof distribution device is different in different clusters, the smaller the fluctuation within the cluster, the greater the degree of random change in the data of some operating parameters collected when the explosion-proof distribution device is running.

[0083] Further, based on the discrete degree and the intra-cluster fluctuation amount, the parameter fluctuation is determined, specifically:

[0084] Calculate the ratio of the discrete degree to the intra-cluster fluctuation, record it as a second ratio, and take the average of the second ratios of all clusters corresponding to each acquisition period as the parameter fluctuation of each acquisition period;

[0085] It should be noted that the greater the parameter fluctuation, the more significant the degree of environmental change inside the explosion-proof distribution device during the corresponding collection period, and the more frequent the disturbances of voltage and power, the more obvious the random change characteristics of the data during the corresponding collection period; when calculating the ratio, in order to avoid the denominator being 0, a preset value greater than 0 is added to the denominator. In this embodiment, the value is 0.01.

[0086] Further, based on the relative difference in the state and the fluctuation degree of the parameter, the state abnormality is determined, specifically:

[0087] The product of the relative state difference and the parameter fluctuation is used as the state abnormality of each acquisition cycle;

[0088] It should be noted that, the greater the state abnormality, the more unstable the operation state of the explosion-proof power distribution device in the corresponding collection period, and the greater the possibility that the explosion-proof power distribution device is in an abnormal operation state.

[0089] Furthermore, the step flow chart of the method for obtaining the state abnormality degree of each acquisition cycle provided by the present application is as follows: Figure 5 shown.

[0090] At this point, the state abnormality degree of each acquisition cycle is obtained.

[0091] Step 3: Analyze the difference between the state abnormality degree of each acquisition cycle and the other acquisition cycles, determine the potential failure coefficient of each acquisition cycle, determine the explosion-proof warning value of each acquisition cycle based on the potential failure coefficient, and evaluate the failure risk of the explosion-proof power distribution device.

[0092] Furthermore, if the explosion-proof power distribution device operates for a long time in a combustible dust, high load, and high voltage environment, the wear of the equipment and the fluctuation of parameters during equipment operation will cause the operating status of the explosion-proof power distribution device in different collection cycles to be quite different. Therefore, by comparing the abnormality of the state in different collection cycles, it is possible to judge whether the overall operation of the explosion-proof power distribution device is stable, specifically:

[0093] Select multiple acquisition cycles during the initial operation of the explosion-proof power distribution device and record them as reference cycles;

[0094] Preferably, in this embodiment, 5 collection cycles during the initial operation of the explosion-proof power distribution device are selected and recorded as reference cycles. As other implementation modes, implementers can set them according to actual conditions.

[0095] Calculate the difference in the state abnormality between each acquisition cycle and any reference cycle, and record it as the abnormality difference; take the sum of the abnormality differences between each acquisition cycle and all reference cycles as the potential failure coefficient of each acquisition cycle;

[0096] Preferably, in this embodiment, the calculation formula of the potential failure coefficient of each acquisition cycle is: ,in, For the The potential failure factor of each acquisition cycle is For the The abnormality of the state of the acquisition cycle, is the state abnormality of the g-th reference cycle, N is the number of all reference cycles, and secondly, For abnormal differences.

[0097] It should be noted that the abnormal difference The larger the value is, the greater the difference in the operating status of the explosion-proof power distribution device under different acquisition cycles. The larger the value is, the higher the potential risk of explosion-proof power distribution device in the corresponding collection period.

[0098] Further, based on the potential failure coefficient, an explosion-proof warning value is determined to evaluate the failure risk of the explosion-proof power distribution device, specifically:

[0099] The calculation method of explosion-proof warning value for each collection cycle is: ,in, For the The explosion-proof warning value of a collection cycle, For the The potential failure factor of each acquisition cycle is is the logarithmic function with base 10.

[0100] If the explosion-proof warning value is greater than the preset threshold, there is a risk of failure in the explosion-proof power distribution device; if the explosion-proof warning value is less than or equal to the preset threshold, the explosion-proof power distribution device operates normally;

[0101] Preferably, in this embodiment, the preset threshold value is 0.8. As other implementation modes, the implementer can set it according to the actual situation.

[0102] If the explosion-proof warning value is greater than a preset threshold, the explosion-proof warning value of each collection cycle is transmitted to the explosion-proof power distribution device through the communication transmission module, and the alarm signal light of the explosion-proof power distribution device is lit for warning.

[0103] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0104] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0105] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. Intelligent explosion-proof power distribution device with integrated wireless transmission function, characterized in that: The device comprises: The data acquisition module is used to obtain each operating parameter of the explosion-proof power distribution device at each moment in each acquisition cycle; A communication transmission module, used to transmit each operating parameter collected by the data collection module to the intelligent monitoring module through a signal transmitter; Intelligent monitoring module to assess whether there is a risk of failure in explosion-proof power distribution devices, including: (1) Clustering the operating parameters at all times in each acquisition cycle to obtain the local density and relative distance corresponding to each time, as well as multiple clusters; determining the relative difference of the state of each acquisition cycle based on the local density and the relative distance; (2) Determine the intra-cluster fluctuation of each cluster corresponding to each acquisition cycle according to the changes of each operating parameter in each cluster corresponding to each acquisition cycle at different times, analyze the discrete distribution of all acquisition moments in different clusters, and determine the parameter fluctuation of each acquisition cycle in combination with the intra-cluster fluctuation; determine the state abnormality of each acquisition cycle based on the relative difference of the state and the parameter fluctuation; (3) Analyze the difference between the state abnormality degree of each collection cycle and the other collection cycles, determine the potential failure coefficient of each collection cycle, determine the explosion-proof warning value of each collection cycle based on the potential failure coefficient, and evaluate the failure risk of the explosion-proof power distribution device.

2. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: Each of the operating parameters includes voltage, power, temperature, humidity and dust concentration.

3. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: The local density and relative distance corresponding to each moment, and the method for obtaining multiple clusters are: All the operating parameters at each moment in each acquisition cycle are combined into a parameter feature vector; Cluster analysis is performed on the parameter feature vectors at all moments in each acquisition cycle to obtain the local density and relative distance corresponding to each moment, as well as multiple clusters.

4. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: Determining the relative difference of the state of each acquisition cycle includes: The ratio of the relative distance to the local density is calculated and recorded as a first ratio, and the average of the first ratio at all times in each acquisition cycle is taken as the relative difference of the state of each acquisition cycle.

5. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: The step of determining the intra-cluster fluctuation amount of each cluster corresponding to each acquisition period includes: Calculate the autocorrelation coefficient of each operating parameter in each cluster at all times; The average of the absolute values ​​of the autocorrelation coefficients of all the operating parameters in each cluster is used as the intra-cluster fluctuation amount of each cluster corresponding to each acquisition period.

6. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 3, characterized in that: Determining the parameter fluctuation of each acquisition cycle includes: Calculate the discrete degree of the time corresponding to all the parameter feature vectors in each cluster; The ratio of the dispersion degree to the intra-cluster fluctuation is calculated and recorded as a second ratio. The average of the second ratios of all clusters corresponding to each acquisition period is taken as the parameter fluctuation of each acquisition period.

7. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: The state abnormality degree of each acquisition cycle is the product of the state relative difference and the parameter fluctuation degree.

8. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: The determining of the potential failure coefficient of each acquisition cycle includes: Select multiple acquisition cycles during the initial operation of the explosion-proof power distribution device and record them as reference cycles; The difference in the state abnormality between each acquisition cycle and any reference cycle is calculated and recorded as the abnormality difference; the sum of the abnormality differences between each acquisition cycle and all reference cycles is taken as the potential failure coefficient of each acquisition cycle.

9. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: The explosion-proof warning value of each acquisition cycle is determined by the following calculation method: ,in, For the The explosion-proof warning value of a collection cycle, For the The potential failure factor of each acquisition cycle is is the logarithmic function with base 10.

10. The intelligent explosion-proof power distribution device with integrated wireless transmission function as claimed in claim 1, characterized in that: The evaluation of the failure risk of the explosion-proof power distribution device includes: if the explosion-proof warning value is greater than a preset threshold, the explosion-proof power distribution device has a failure risk, otherwise, the explosion-proof power distribution device operates normally.

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