A UHV intelligent ice melting device

Abstract

The present invention relates to the field of knife switch equipment, and in particular to an ultra-high voltage intelligent ice-melting device. It comprises: a first chamber, a second chamber, a third chamber, an upper outlet terminal, a lower outlet terminal, a driving mechanism, a transmission mechanism and a knife switch mechanism; the two ends of the transmission mechanism are respectively connected to the driving mechanism and the knife switch mechanism, and are used to control the contact and separation of the moving contact and the static contact in the connection of the knife switch mechanism; the two ends of the second chamber are respectively connected to the first chamber and the third chamber to enclose and form a closed internal cavity, and the knife switch mechanism, the transmission mechanism and the driving mechanism are all arranged in the internal cavity. The driving mechanism, the transmission mechanism and the knife switch mechanism in the ultra-high voltage intelligent ice-melting device of the present invention are all encapsulated inside a closed space. As a result, the opening and closing operations are not affected by the external environment and ice coating, and the problem of difficulty in completing the closing and opening of the knife switch during ice coating can be effectively solved.

Description

Background Art

[0002] Under extreme climatic conditions, some power transmission lines in my country are prone to ice. Ice on transmission lines will cause excessive load on the towers, and the conductors and ground wires are prone to jumping when the ice falls off. The longitudinal unbalanced tension caused by uneven ice on the conductors and ground wires will cause the towers to tilt or bend toward the side with greater tension. When the designed bearing capacity is exceeded, the conductors and ground wires will break, the hardware will fall off, etc. In severe cases, the towers will collapse, seriously endangering the safe operation of the power system.

[0003] In order to solve the hazards of ice coating on the above-mentioned lines, it is necessary to melt the ice on the ground wire. The ground wire is used as a load, and two phases of the three-phase conductor form a loop with it. The DC ice melting device in the power station applies current to heat the ground wire to melt the ice and snow. In this process, an ultra-high voltage intelligent ice melting device is needed to control the contact between the moving contact and the static contact to connect the circuit. Specifically, the moving contact is moved up and down by the motor to control the transmission mechanism, and the contact and separation of the moving contact and the static contact are completed, that is, the closing and opening of the ultra-high voltage intelligent ice melting device is completed.

[0004] The moving contacts, stationary contacts, drive mechanisms and transmission mechanisms in the existing UHV intelligent ice melting devices are all exposed to the air. On the one hand, the moisture in the air will affect the contact resistance of the moving contacts. On the other hand, when ice is accumulated, the ice will freeze the moving contacts, making it difficult to complete the knife switch closing action. The motor and transmission mechanism are also exposed to the outside and will be covered with ice. When the ice is melted, it is also difficult for the transmission mechanism to complete the knife switch closing action. Summary of the invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is:

[0006] According to one aspect of the present invention, there is provided a UHV intelligent ice melting device, comprising: a first chamber, a second chamber, a third chamber, an upper outlet terminal, a lower outlet terminal, a driving mechanism, a transmission mechanism and a knife switch mechanism;

[0007] The two ends of the transmission mechanism are respectively connected to the driving mechanism and the knife switch mechanism, and are used to control the contact and separation of the moving contact and the static contact in the connection of the knife switch mechanism;

[0008] The two ends of the second chamber are respectively connected to the first chamber and the third chamber to enclose a closed internal cavity, and the knife switch mechanism, the transmission mechanism and the drive mechanism are all arranged in the internal cavity; the upper outlet terminal is fixedly arranged at the upper end of the first chamber, a conductive flange is sandwiched between the first chamber and the second chamber, and the lower outlet terminal is conductively connected to the conductive flange;

[0009] The knife switch mechanism includes a moving contact, a stationary contact and a vacuum interrupter;

[0010] The moving contact and the static contact are arranged opposite to each other in the vacuum arc extinguishing chamber, the vacuum arc extinguishing chamber is arranged inside the first chamber, the static contact is conductively connected to the upper outlet terminal, the moving contact is fixedly connected to the movable end of the transmission mechanism, and the moving contact is conductively connected to the conductive flange, which is used to conductively connect the moving contact to the lower outlet terminal.

[0011] Furthermore, the transmission mechanism includes: a conductive clamp, a soft-connected conductive ring, a fixed pressure ring and an insulating pull rod;

[0012] The fixed pressure ring is fixedly inserted into the conductive flange, and a clamping ring is arranged on the outer wall of the fixed pressure ring. One end of the conductive clamp is embraced on the outer wall of the external conductive rod of the moving contact, and the other end of the conductive clamp is clamped between the clamping ring and the conductive flange; the movable end of the insulating pull rod passes through the fixed pressure ring and is fixedly connected with the end of the external conductive rod of the moving contact; the soft-connecting conductive ring is sleeved on the insulating pull rod, and the upper and lower side walls of the soft-connecting conductive ring are respectively arranged to abut against the end of the external conductive rod of the moving contact and the upper end face of the fixed pressure ring.

[0013] Further, the driving mechanism includes: an electric cylinder, a first power supply and a control device;

[0014] The movable end of the electric cylinder is elastically connected to the connecting end of the insulating pull rod through a spring; the control device is communicatively connected to the electric cylinder, and the first power supply is conductively connected to the control device and the electric cylinder respectively.

[0015] Furthermore, the driving mechanism also includes: a mechanical power generation device and a second power source;

[0016] The mechanical power generation device is conductively connected to the second power supply, and the second power supply is conductively connected to the control device and the electric cylinder respectively.

[0017] Furthermore, the structural parameters of the moving contact, the stationary contact and the vacuum interrupter are obtained according to the following steps:

[0018] According to the similarity between the installation environment vector of the ice melting device setting area and multiple cluster environment vectors, the target correlation sequence of the structural parameters of the moving contact, the static contact and the vacuum arc extinguishing chamber in the ice melting device and the arc extinguishing time is obtained; the target correlation sequence is the correlation sequence corresponding to the cluster environment vector with the highest similarity; the correlation sequence A=(A1, A2, …, Ai, …, Az), where Ai is the correlation coefficient between the i-th structural parameter and the arc extinguishing time in the corresponding use environment; z is the total number of types of structural parameters of the moving contact, the static contact and the vacuum arc extinguishing chamber, i=1, 2, …, z;

[0019] The installation environment vector includes the average temperature and humidity, the average ice thickness and the average ice duration in the corresponding period of time when the line in the corresponding installation area is covered with ice; the structural parameters of the moving contact, the static contact and the vacuum interrupter include: the size of the moving contact and the conductivity, melting point and erosion mass loss rate of the material, the size of the static contact and the conductivity, melting point and erosion mass loss rate of the material, the contact disconnection speed and the internal diameter, vacuum degree and conductivity and melting point of the shielding layer material of the vacuum interrupter. Different cluster environment vectors are used to represent the installation environment vectors corresponding to different types of icing environments, and the cluster environment vector and the installation environment vector include the same elements.

[0020] Obtaining initial parameter information, the initial parameter information includes an initial structural parameter sequence and an adjustment range of each structural parameter; the initial structural parameter sequence B=(B1, B2, …, Bi, …, Bz), where Bi is the initial parameter value of the i-th structural parameter;

[0021] The particle swarm optimization algorithm and target correlation sequence are used to optimize the initial parameter information, and the structural parameters of the moving contact, static contact and vacuum arc chamber in the ice melting device used in the setting area are generated; the absolute value of the correlation coefficient between each structural parameter in the target correlation sequence and the arc extinguishing time is used in the optimization process to adjust the self-learning coefficient of the corresponding structural parameter in the initial structural parameter sequence, and the absolute value of the correlation coefficient of the same structural parameter is positively correlated with the self-learning coefficient.

[0022] Furthermore, the particle swarm optimization algorithm and the target correlation sequence are used to optimize the initial parameter information, including:

[0023] According to the target correlation sequence, the self-learning coefficient and social learning coefficient corresponding to each structural parameter in the initial structural parameter sequence are generated; wherein the self-learning coefficient c1i and social learning coefficient c2i corresponding to the i-th structural parameter in the initial structural parameter sequence meet the following conditions:

[0024] ;

[0025] ;

[0026] Among them, |D| i is the absolute value of the correlation coefficient corresponding to the i-th structural parameter in the target correlation sequence; |D| max and |D| min are the maximum and minimum absolute values ​​of the correlation coefficient in the target correlation sequence, respectively;

[0027] According to the self-learning coefficient and social learning coefficient corresponding to each structural parameter, the particle swarm optimization algorithm is used to adjust the initial value of each structural parameter in the initial structural parameter sequence.

[0028] Furthermore, the initial parameter information is optimized using a particle swarm optimization algorithm and a target correlation sequence, and further includes:

[0029] According to the adjustment cost corresponding to each structural parameter when it is adjusted, the inertia weight corresponding to each structural parameter in the initial structural parameter sequence is generated; the adjustment cost corresponding to each structural parameter is negatively correlated with the inertia weight, and the adjustment cost includes material cost, process cost and production line adjustment cost;

[0030] According to the inertia weight, self-learning coefficient and social learning coefficient corresponding to each structural parameter, the particle swarm optimization algorithm is used to adjust the initial value of each structural parameter in the initial structural parameter sequence.

[0031] Furthermore, the inertia weight wi corresponding to the i-th structural parameter in the initial structural parameter sequence satisfies the following conditions:

[0032] ;

[0033] Among them, CL i , GY i and CX i They are respectively the eigenvalue corresponding to the material cost, the eigenvalue corresponding to the process cost and the eigenvalue corresponding to the production line adjustment cost when the i-th structural parameter in the initial structural parameter sequence is adjusted.

[0034] Furthermore, the correlation sequence corresponding to each cluster of environment vectors is obtained according to the following steps:

[0035] Clustering is performed on the installation environment vectors corresponding to multiple different areas to generate multiple environment clusters; the cluster environment vector is the center vector in the environment cluster;

[0036] The area corresponding to the member of each environment cluster closest to the cluster environment vector is taken as the target test area;

[0037] Each test ice-melting device is set in each target test area, and the average arc-extinguishing time corresponding to each test ice-melting device is obtained; the test ice-melting device has the same structure as the UHV intelligent ice-melting device; and the structural parameters of the moving contact, the static contact and the vacuum arc-extinguishing chamber in different test ice-melting devices are different;

[0038] According to the structural parameter sequence and average arc extinguishing time corresponding to each test ice melting device, a correlation sequence corresponding to the structural parameters under the corresponding environment of each target test area is generated; wherein, the correlation sequence A corresponding to the structural parameters under the corresponding environment of the yth target test area is y =(A y 1. A y 2. ..., A y i, …, Ay z), A y A i is the correlation coefficient between the i-th structural parameter and the arc extinguishing time in the use environment corresponding to the y-th target test area; y i satisfies the following conditions:

[0039] ;

[0040] Among them, d m i is the parameter value of the i-th structural parameter in the structural parameter sequence corresponding to the m-th test ice-melting device in the use environment corresponding to the y-th target test area; AVG d i mh is the average value of the i-th structural parameter in the use environment corresponding to the y-th target test area; m AVG is the average arc extinguishing time corresponding to the mth test ice melting device in the use environment corresponding to the yth target test area; mh is the mean of the average arc extinguishing time of all tested ice-melting devices in the use environment corresponding to the yth target test area; Q is the total number of tested ice-melting devices, m=1, 2, …, Q; g is the total number of target test areas, y=1, 2, …, g.

[0041] The correlation sequence under the environment corresponding to each target test area is used as the correlation sequence corresponding to the cluster environment vector corresponding to each target test area.

[0042] Furthermore, a plurality of umbrella skirt edges are arranged at intervals on the outer side walls of the first chamber and the second chamber.

[0043] The present invention has at least the following beneficial effects:

[0044] The driving mechanism, transmission mechanism and knife switch mechanism in the UHV intelligent ice melting device of the present invention are all encapsulated in a closed space. The driving mechanism in the closed space is controlled by a control box, so that the two action contacts in the knife switch mechanism are in contact and separated up and down, thereby realizing the closing and opening of the knife switch. As a result, the closing and opening operations are not affected by the external environment and ice coating, and the problem of difficulty in closing and opening the knife switch during ice coating can be effectively solved. The product has stable functions, simple and smooth operation, and greatly improves the construction efficiency of ground wire ice melting. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 A schematic diagram of the overall structure of an ultra-high voltage intelligent ice melting device provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the internal cross-sectional structure of an ultra-high voltage intelligent ice melting device provided by an embodiment of the present invention;

[0048] Figure 3 for Figure 2 A schematic diagram of the local enlarged structure at point A in the middle;

[0049] Figure 4 The present invention is a flowchart of the steps for obtaining the structural parameters of the moving contact, the stationary contact and the vacuum interrupter provided in one embodiment of the invention.

[0050] Reference numerals

[0051] 10. Vacuum interrupter; 11. Static contact; 12. Moving contact; 20. Conductive clip; 21. Flexible conductive ring; 22. Fixed pressure ring; 23. Clamping ring; 24. Conductive flange; 30. Insulating pull rod; 31. Spring; 41. Electric cylinder; 50. First chamber; 51. Second chamber; 52. Third chamber; 53. Umbrella skirt; 54. Mechanical power generation device; 60. Upper outlet terminal; 61. Lower outlet terminal. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0053] As a possible embodiment of the present invention, Figures 1 to 3 As shown, a UHV intelligent ice melting device is provided, comprising: a first chamber 50, a second chamber 51, a third chamber 52, an upper outlet terminal 60, a lower outlet terminal 61, a driving mechanism, a transmission mechanism and a knife switch mechanism.

[0054] The two ends of the transmission mechanism are respectively connected to the driving mechanism and the knife switch mechanism, and are used to control the contact and separation of the moving contact 12 and the static contact 11 in the knife switch mechanism connection, so as to realize the on and off of the ice melting circuit. The two ends of the second chamber 51 are respectively connected to the first chamber 50 and the third chamber 52 to enclose and form a closed internal cavity, so that the driving mechanism, the transmission mechanism and the knife switch mechanism are arranged in the internal cavity, thereby preventing ice from being covered, so as to ensure that the knife switch mechanism can be opened and closed smoothly.

[0055] Specifically, the first chamber 50 and the second chamber 51 are both made of insulating silicone rubber composite materials, which can improve the isolation effect of external moisture. At the same time, multiple umbrella skirt edges 53 are arranged at intervals on the outer walls of the first chamber 50 and the second chamber 51. The umbrella skirt edge 53 has the same structure as the skirt edge arranged on the outside of the insulator on the existing high-voltage line. Its main function is to increase the creepage distance and prevent electrical flashover (i.e., the phenomenon of arc crossing the surface of the insulator) caused by environmental factors such as moisture and dirt.

[0056] like Figure 2 As shown, the upper outlet terminal 60 is fixedly arranged at the upper end of the first chamber 50, a conductive flange 24 is sandwiched between the first chamber 50 and the second chamber 51, and the lower outlet terminal 61 is conductively connected to the conductive flange 24. The upper outlet terminal 60 and the lower outlet terminal 61 are respectively used to connect different line segments in the ice melting circuit, and the specific configuration can be determined by those skilled in the art according to the actual configuration of the ice melting circuit.

[0057] like Figure 2 As shown, the knife switch mechanism includes a moving contact 12, a stationary contact 11 and a vacuum interrupter 10.

[0058] The moving contact 12 and the static contact 11 are arranged opposite to each other in the vacuum interrupter 10, and the vacuum interrupter 10 is arranged inside the first chamber 50. The static contact 11 is conductively connected to the upper outlet terminal 60, the moving contact 12 is fixedly connected to the movable end of the transmission mechanism, and the moving contact 12 is conductively connected to the conductive flange 24, which is used to conductively connect the moving contact 12 to the lower outlet terminal 61.

[0059] Because, when the knife switch mechanism (disconnector) needs to disconnect the current, especially under load, an arc will be generated at the moment when the moving contact 12 is separated from the static contact 11. This arc may not only cause damage to the equipment, but also cause safety accidents. The vacuum arc extinguishing chamber 10 uses vacuum (an excellent insulating medium) to quickly interrupt and extinguish the arc. Since the vacuum has a high dielectric strength, it means that even under high voltage conditions, it can effectively prevent electrical breakdown, thereby ensuring the safety of electrical isolation. At the same time, in a vacuum, once the arc is generated, it will quickly spread and cool down, because there is a lack of gas molecules that can keep the arc alive in the vacuum environment. This allows the arc to be extinguished in a very short time, usually within a few milliseconds. In addition, the vacuum arc extinguishing chamber 10 is usually equipped with a metal shielding cover, which can not only absorb the heat generated by the arc, but also prevent any trace ions that may remain from returning to the contact area, further ensuring the effective extinguishing of the arc, thereby improving the safety of the equipment.

[0060] like Figures 2 to 3 As shown, the transmission mechanism includes: a conductive clamp 20, a soft-connecting conductive ring 21, a fixed pressure ring 22 and an insulating pull rod 30.

[0061] The fixed pressure ring 22 is fixedly installed on the conductive flange 24, and a clamping ring 23 is arranged on the outer side wall of the fixed pressure ring 22. One end of the conductive clamp 20 is arranged on the outer side wall of the external conductive rod of the moving contact 12, and the other end of the conductive clamp 20 is clamped between the clamping ring 23 and the conductive flange 24. The movable end of the insulating pull rod 30 passes through the fixed pressure ring 22 and is fixedly connected to the end of the external conductive rod of the moving contact 12. The flexible connection conductive ring 21 is sleeved on the insulating pull rod 30, and the upper and lower side walls of the flexible connection conductive ring 21 are respectively set to abut against the end of the external conductive rod of the moving contact 12 and the upper end surface of the fixed pressure ring 22.

[0062] The transmission mechanism in this embodiment is mainly used to drive the moving contact 12 to move up and down to achieve contact and separation between the moving contact 12 and the stationary contact 11. At the same time, the transmission mechanism also needs to ensure that the moving contact 12 and the lower outlet terminal 61 are always in a conductive connection state.

[0063] Specifically, in this embodiment, a conductive clip 20, a soft-connecting conductive ring 21 and a fixed pressing ring 22 are provided in the transmission mechanism to form two connection paths, so as to ensure that the moving contact 12 and the lower outlet terminal 61 are always in a conductive connection state.

[0064] First, one end of the conductive clip 20 is embraced on the side wall of the external conductive rod of the moving contact 12, and the other end is clamped between the clamping ring 23 and the conductive flange 24. Since the material of the conductive clip 20 itself is conductive, the moving contact 12 can be connected to the lower outlet terminal 61.

[0065] Second, the upper and lower side walls of the soft connection conductive ring 21 are respectively set to abut against the end of the external conductive rod of the moving contact 12 and the upper end face of the fixed pressure ring 22. Even when the moving contact 12 moves up and down, the soft connection conductive ring 21 can make the two ends close to the corresponding positions through its own elastic deformation ability, so as to achieve the purpose of conductive connection between the moving contact 12 and the lower outlet terminal 61. Specifically, the soft connection conductive ring 21 can be made of copper. In addition, the soft connection conductive ring 21 can also be replaced by a wire, and the two ends of the wire are respectively connected to the end of the external conductive rod of the moving contact 12 and the upper end face of the fixed pressure ring 22.

[0066] The insulating rod 30 is made of insulating material, which can effectively insulate and separate the knife switch mechanism from the driving mechanism. In addition, a sleeve made of insulating silicone rubber composite material is sleeved on the outside of the insulating rod 30 to further improve the insulation capacity.

[0067] like Figure 1 and Figure 2 As shown, the driving mechanism includes: an electric cylinder 41, a first power supply and a control device.

[0068] The movable end of the electric cylinder 41 is elastically connected to the connecting end of the insulating pull rod 30 through a spring 31. Since a large pulling force is required to separate the moving contact 12 from the stationary contact 11 when the moving contact 12 is separated from the stationary contact 11, the insulating pull rod 30 will generate a large downward impact force after separation. In order to reduce the impact damage of the impact force on the electric cylinder 41, a pressure ring is provided for buffering.

[0069] The control device is connected to the electric cylinder 41 for communication, and the first power supply is respectively connected to the control device and the electric cylinder 41 for conductive connection. The first power supply may be a power line drawn from a power grid line or other power station line.

[0070] Furthermore, the driving mechanism also includes: a mechanical power generation device 54 and a second power source.

[0071] The mechanical power generator 54 is conductively connected to the second power source, and the second power source is conductively connected to the control device and the electric cylinder 41 respectively.

[0072] Two different power sources are provided for power supply, and the second power source can be charged by a mechanical power generation device 54 (an existing foot-operated mechanical power generation device). Thus, when the first power source fails to supply power, the mechanical power generation device 54 can be manually operated to charge the second power source to ensure the normal power supply of the driving power source, thereby enabling the normal opening and closing of the knife switch mechanism.

[0073] Since the current in the ice melting circuit is extremely large, an arc will be generated when the moving contact 12 and the static contact 11 are separated. Since the arc has a high temperature, it is easy to cause certain high-temperature corrosion to the moving contact 12 and the static contact 11 when it exists. The moving contact 12, the static contact 11 and the vacuum interrupter 10 manufactured with different structural parameters will have different performances in arc extinguishing time (that is, the time from the generation to the extinction of the arc) in different use environments. Therefore, in order to minimize the arc extinguishing time in different use environments, it is necessary to determine the structural parameters of the moving contact 12, the static contact 11 and the vacuum interrupter 10 according to the following method.

[0074] In another possible embodiment of the present invention, Figure 4 As shown, the structural parameters of the moving contact 12, the stationary contact 11 and the vacuum interrupter 10 are obtained according to the following steps:

[0075] S100: According to the similarity between the installation environment vector of the ice melting device setting area and multiple cluster environment vectors, obtain the target correlation sequence of the structural parameters and arc extinguishing time of the moving contact 12, the static contact 11 and the vacuum interrupter 10 in the ice melting device. The target correlation sequence is the correlation sequence corresponding to the cluster environment vector with the highest similarity. Correlation sequence A=(A1, A2, ..., Ai, ..., Az), where Ai is the correlation coefficient between the i-th structural parameter and the arc extinguishing time in the corresponding use environment. z is the total number of types of structural parameters of the moving contact 12, the static contact 11 and the vacuum interrupter 10, i=1, 2, ..., z.

[0076] In this embodiment, the cluster environment vector is the center vector in the environment cluster. The installation environment vector has the same element type as the cluster environment vector, such as the elements corresponding to the average temperature and average humidity, average ice thickness and average ice duration in the corresponding period of time when the line in the corresponding installation area is covered with ice. The structural parameters of the moving contact 12, the static contact 11 and the vacuum interrupter 10 include: the volume size of the moving contact 12 and the conductivity, melting point and erosion mass loss rate of the material, the volume size of the static contact 11 and the conductivity, melting point and erosion mass loss rate of the material, the contact disconnection speed and the inner diameter, vacuum degree and conductivity and melting point of the shielding layer material of the vacuum interrupter 10. The erosion mass loss rate is the reduction in material weight per unit arc erosion time. In this embodiment, the length of time the arc is generated will be affected by the contact, the contact disconnection speed and the vacuum interrupter 10. Since the arc generation formula mainly forms electrical erosion and high-temperature erosion, the parameters corresponding to electrical erosion and high-temperature erosion can be adjusted to affect the arc extinguishing time of the arc.

[0077] Specifically, the contact is mainly affected by the size of the contact and the arc erosion resistance and high temperature resistance of the contact material. The vacuum interrupter 10 is mainly affected by the inner diameter of the vacuum interrupter 10, the vacuum degree, and the conductivity and melting point of the shielding layer material.

[0078] Different cluster environment vectors are used to represent the installation environment vectors corresponding to different icing types, and the cluster environment vectors and the installation environment vectors include the same elements. Specifically, the similarity calculation between the installation environment vector and multiple cluster environment vectors can be calculated using cosine similarity. Specifically, the installation environment vector and multiple cluster environment vectors are mainly used to reflect the climate environment characteristics and icing characteristics when the line icing phenomenon is formed in the corresponding use area. In this embodiment, it is mainly characterized by four types of elements such as average temperature and average humidity, average icing thickness and average icing duration. In actual use scenarios, it can also be increased according to needs, such as adding average light duration, average wind speed and other characteristics. Due to the different environmental characteristics and icing characteristics in different use environments, the setting method of the ice melting circuit and the working conditions of the ultra-high voltage intelligent ice melting device will be different. As a result, the arc extinguishing duration of ultra-high voltage intelligent ice melting devices with different structural parameters will have different performances when used in different use environments. Therefore, in this embodiment, the target correlation sequence is first obtained by the matching degree between the installation environment vector and the cluster environment vector, so that in the subsequent particle swarm optimization processing, the self-learning coefficient c1i and the social learning coefficient c2i corresponding to each structural parameter are adjusted according to the target correlation sequence, so as to obtain the corresponding structural parameters more accurately.

[0079] Specifically, in this embodiment, the correlation sequence corresponding to each cluster of environment vectors is obtained according to the following steps:

[0080] S101: performing clustering processing on installation environment vectors corresponding to a plurality of different regions to generate a plurality of environment clusters.

[0081] First, the historical record data of the line icing phenomenon area in my country can be used to obtain the four types of element values ​​in the corresponding installation environment vector, such as the average temperature and average humidity, the average icing thickness and the average icing duration. Then, a large number of installation environment vectors obtained will be clustered. Specifically, the clustering method in this embodiment can use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise, a density-based clustering algorithm) clustering algorithm or the OPTICS (Ordering Points To Identify the Clustering Structure) clustering algorithm for clustering to generate multiple environment clusters.

[0082] S102: The area corresponding to the member closest to the cluster environment vector (ie, the center vector) in each environment cluster is used as the target test area.

[0083] S103: Each test ice melting device is set in each target test area, and the average arc extinguishing time corresponding to each test ice melting device is obtained. The test ice melting device has the same structure as the UHV intelligent ice melting device. In addition, the structural parameters of the moving contact 12, the static contact 11 and the vacuum interrupter 10 in different test ice melting devices are different.

[0084] In this embodiment, when ice appears on the line in each target test area, different test ice-melting devices can be used to control the ice-melting circuits of different line sections to collect a large amount of average arc-extinguishing time data corresponding to the test ice-melting devices in different environments.

[0085] S104: Generate a correlation sequence corresponding to the structural parameters under the environment corresponding to each target test area according to the structural parameter sequence and the average arc extinguishing time corresponding to each test ice melting device. Wherein, the correlation sequence corresponding to the structural parameters under the environment corresponding to the yth target test area is Ay=(Ay1、Ay2、…、Ay3、Ay4、Ay5、Ay6、Ay7、Ay8、Ay9、Ay10、Ay11、Ay12、Ay13、Ay14、Ay15、Ay16、Ay17、Ay18、Ay19、Ay210、Ay211、Ay22、Ay33、Ay34、Ay35、Ay36、Ay37、Ay38、Ay39、Ay40、Ay41、Ay42、Ay43、Ay44、Ay45、Ay46、Ay47、A y i, …, A y z), A y i is the correlation coefficient between the i-th structural parameter and the arc extinguishing time in the use environment corresponding to the y-th target test area. y i satisfies the following conditions:

[0086] ;

[0087] Among them, d m i is the parameter value of the i-th structural parameter in the structural parameter sequence corresponding to the m-th test ice-melting device in the use environment corresponding to the y-th target test area; AVG d i mh is the average value of the i-th structural parameter in the use environment corresponding to the y-th target test area; m AVG is the average arc extinguishing time corresponding to the mth test ice melting device in the use environment corresponding to the yth target test area; mh is the mean of the average arc extinguishing time of all tested ice-melting devices in the use environment corresponding to the yth target test area; Q is the total number of tested ice-melting devices, m=1, 2, …, Q; g is the total number of target test areas, y=1, 2, …, g.

[0088] Since each structural parameter in this embodiment is a continuously variable value, the correlation between each structural parameter and the average arc extinguishing time under each environment can be calculated by the Pearson correlation coefficient. The value of the correlation coefficient in this embodiment is between -1 and +1, where: if the value is a positive value, it means that the structural parameter is completely positively correlated with the average arc extinguishing time. 0 means there is no linear correlation: there seems to be no direct relationship between the changes in the two variables. If the value is a negative value, it means that the structural parameter is completely negatively correlated with the average arc extinguishing time, and the absolute value of the correlation coefficient indicates the degree of correlation.

[0089] After the above S104 processing, a correlation matrix E between the structural parameters and the environment can be generated. E can be in the following form:

[0090] .

[0091] S105: Using the correlation sequence under the environment corresponding to each target test area as the correlation sequence corresponding to the cluster environment vector corresponding to each target test area.

[0092] S200: Acquire initial parameter information, which includes an initial structural parameter sequence and an adjustment range of each structural parameter. The initial structural parameter sequence B = (B1, B2, ..., Bi, ..., Bz), where Bi is the initial parameter value of the i-th structural parameter.

[0093] The initial structural parameter sequence in this embodiment can be a parameter sequence corresponding to any test ice melting device, and the adjustment range of each structural parameter can be determined according to the data adjustable range of each parameter in actual use, thereby limiting the adjustable range of each structural parameter, that is, limiting the optimization range of the particle swarm optimization algorithm. In addition, the corresponding installation environment vector in the setting area can also be used as a restriction condition parameter in the initial parameter information.

[0094] S300: Use the particle swarm optimization algorithm and the target correlation sequence to optimize the initial parameter information, and generate the structural parameters of the moving contact 12, the static contact 11 and the vacuum interrupter 10 in the ice melting device used in the setting area. In the optimization process, the absolute value of the correlation coefficient between each structural parameter and the arc extinguishing time in the target correlation sequence is used to adjust the self-learning coefficient of the corresponding structural parameter in the initial structural parameter sequence, and the absolute value of the correlation coefficient of the same structural parameter is positively correlated with the self-learning coefficient.

[0095] Particle Swarm Optimization (PSO) is a random search technology based on swarm intelligence. The algorithm is inspired by the behavior of bird groups and the concept of social sharing and is used to solve parameter optimization problems. Specifically, each structural parameter in this embodiment is equivalent to a "particle" in the search space, and the search space corresponding to each "particle" is the adjustment range of the parameters in the initial parameter information. In addition, each particle in the algorithm adjusts its speed and direction according to its own best position (pBest) and the best position (gBest) of the entire group. In the process of updating the parameter value, it is mainly affected by the three coefficients of inertia weight, self-learning coefficient and social learning coefficient.

[0096] In this embodiment, the inertia weight, the self-learning coefficient and the social learning coefficient are determined in the following manner:

[0097] S301: Generate the self-learning coefficient and social learning coefficient corresponding to each structural parameter in the initial structural parameter sequence according to the target correlation sequence. The self-learning coefficient c1i and social learning coefficient c2i corresponding to the i-th structural parameter in the initial structural parameter sequence meet the following conditions:

[0098] ;

[0099] .

[0100] Among them, |D| i is the absolute value of the correlation coefficient corresponding to the i-th structural parameter in the target correlation sequence. |D| max and |D| min are the maximum and minimum absolute values ​​of the correlation coefficient in the target correlation sequence, respectively.

[0101] S302: According to the self-learning coefficient and the social learning coefficient corresponding to each structural parameter, the particle swarm optimization algorithm is used to adjust the initial value of each structural parameter in the initial structural parameter sequence.

[0102] Since the self-learning coefficient, i.e., c1i, mainly affects the self-learning ability of the particle, i.e., it mainly increases the influence of the particle's own historical best position (its own optimal solution) on the final solution, if a particle finds a high-quality solution in its search history, then it will be more likely to return to or approach that position, thereby increasing the possibility of finding a better solution. In other words, if the self-learning coefficient is higher, it means that the optimal solution of the structural parameter will have a greater impact on finding the final solution, that is, in the search process, more attention should be paid to the ability of the structural parameter to find its own optimal value.

[0103] On the contrary, the social learning coefficient, i.e., c2i, is mainly used to promote global information sharing, so that the entire group can converge to the potential global optimal solution more quickly. It reduces the influence of the particle's own historical best position (its own optimal solution) on the final solution. In other words, if the social learning coefficient is higher, it means that the optimal solution of the structural parameter will have a smaller impact on finding the final solution, which means that during the search process, the focus on finding the optimal value of the structural parameter should be reduced.

[0104] Based on the above, the influence of the social learning coefficient and the self-learning coefficient, if the absolute value of the correlation coefficient corresponding to a certain structural parameter in the target correlation sequence is larger, the influence of the structural parameter on the arc extinguishing time will be greater, that is, the optimal solution of the structural parameter will have a greater impact on finding the final solution, so the corresponding self-learning coefficient will be larger, and conversely, the corresponding social learning coefficient will be smaller. Therefore, based on the calculation method in S301, the above effect can be achieved.

[0105] S303: Generate an inertia weight corresponding to each structural parameter in the initial structural parameter sequence according to the adjustment cost corresponding to each structural parameter when it is adjusted. The adjustment cost corresponding to each structural parameter is negatively correlated with the inertia weight, and the adjustment cost includes material cost, process cost and production line adjustment cost.

[0106] Specifically, the inertia weight wi corresponding to the i-th structural parameter in the initial structural parameter sequence satisfies the following conditions:

[0107] .

[0108] Among them, CL i , GY i and CX i They are respectively the eigenvalue corresponding to the material cost, the eigenvalue corresponding to the process cost and the eigenvalue corresponding to the production line adjustment cost when the i-th structural parameter in the initial structural parameter sequence is adjusted.

[0109] In the particle swarm optimization algorithm (PSO), the inertia weight wi (i.e., the size of the inertia part) is crucial to balancing global search and local search. The inertia weight determines the degree of influence of the previous speed on the current speed, thereby affecting the ability of particles to explore new areas and the speed of convergence to the optimal solution. Specifically, the inertia weight in this embodiment ranges from (0,1), such as wi=0.9 or wi=0.729. A larger inertia weight is conducive to global search, while a smaller inertia weight is conducive to fine-tuning and local search.

[0110] Based on this, in this embodiment, if the parameter value of a certain structural parameter is modified and changes, the amount of resources consumed is large, then it should be assigned a smaller inertia weight, so that it can select a smaller value range within the initial value adjustment range for fine adjustment and local search, and then the parameter value of the structural parameter will be adjusted within a smaller range to further reduce the cost of adjusting the value. On the contrary, if the parameter value of a certain structural parameter is modified and changes, the amount of resources consumed is small, then it should be assigned a larger inertia weight so that it can search for values ​​within a larger value range (that is, global search).

[0111] Because, when adjusting a certain structural parameter, it usually involves changes in the preparation process, changes in the production line, and changes in the quality of the materials used. If you want to reduce the electro-erosion mass loss rate of the contact, you may need to use more advanced processing technology, and at the same time, you need to adjust the relevant equipment in the production line, and even introduce new materials. These changes will lead to an increase in cost, so in this embodiment, the overall cost is mainly measured by the cost changes in the three dimensions of material cost, process cost, and production line adjustment cost. In addition, the overall cost is inversely proportional to the inertia weight. Therefore, if the overall cost is smaller when the parameter value of a certain structural parameter is modified, it can be assigned a larger inertia weight, and vice versa.

[0112] In addition, as another embodiment of a method for determining an inertia weight, this embodiment further provides the following method for determining an inertia weight, which is used for the method for determining an inertia weight in S303. Specifically, in another method for determining an inertia weight, wi satisfies the following conditions:

[0113] ;

[0114] Among them, w i start is the initial inertia weight corresponding to the i-th structural parameter in the initial structural parameter sequence; w i end is the final inertia weight corresponding to the i-th structural parameter in the initial structural parameter sequence; T is the total number of iterations, and t is the current number of iterations. i start With w i end It can be set manually according to actual conditions.

[0115] In order to better balance global search and local search, the method in this embodiment is to reduce the inertia weight linearly over time (number of iterations). A larger inertia weight is used at the beginning to promote extensive exploration, and the weight is gradually reduced during the iteration process to more accurately approach the optimal solution.

[0116] S304: According to the inertia weight, self-learning coefficient and social learning coefficient corresponding to each structural parameter, the particle swarm optimization algorithm is used to adjust the initial value of each structural parameter in the initial structural parameter sequence.

[0117] In this embodiment, the self-learning coefficient and the social learning coefficient are generated by the correlation coefficient corresponding to each structural parameter of the target correlation sequence, and the corresponding inertia weight is determined according to the change cost corresponding to each structural parameter. Compared with the prior art, the inertia weight, self-learning coefficient and social learning coefficient of the present invention are more targeted, and can more accurately control the direction and speed of each structural parameter during the update iteration, so as to obtain the optimal parameter value corresponding to each structural parameter more quickly and accurately.

[0118] Specifically, the particle swarm optimization algorithm (PSO) of this embodiment includes three parts: initialization part, objective function and PSO main loop. Among them, the inertia weight, self-learning coefficient and social learning coefficient setting of the initialization part are different from those in the prior art, and the objective function and PSO main loop are the same as those in the prior art. In addition, this embodiment provides a Python implementation example of the possible initialization part and the objective function part:

[0119] import numpy as np

[0120] # PSO parameter settings

[0121] num_particles = 30

[0122] num_iterations = 100

[0123] search_space_dim = 2 # Search space dimension

[0124] # Inertia weight

[0125] # Cognitive coefficient, also known as self-learning coefficient

[0126] # Social coefficient, also known as social learning coefficient

[0127] # Initialize particle position and velocity

[0128] np.random.seed(42) # fix the random seed so that the results are reproducible

[0129] positions = np.random.rand(num_particles, search_space_dim) 10 - 5 #The initial position is between [-5, 5]

[0130] velocities = np.zeros_like(positions)

[0131] # Initialize individual best position and personal best fitness

[0132] p_best_positions = positions.copy()

[0133] p_best_scores = np.full(num_particles, float('inf'))

[0134] # Initialize the global best position and global best fitness

[0135] g_best_position = positions[0]

[0136] g_best_score = float('inf')

[0137] #Definition of target function (here we take Sphere function as an example)

[0138] def objective_function(x):

[0139] return np.sum( , axis=1)

[0140] Initialization part: defines basic parameters such as the size of the particle swarm, the number of iterations, the dimension of the search space, and randomly initializes the position and velocity of the particles. Objective function: In this example, the Sphere function is used as the objective function, which is a standard function commonly used to test optimization algorithms. The form of the Sphere function is: ; where x p is the p-th dimensional vector, and k is the total dimension of the vector.

[0141] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0142] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0143] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0144] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0145] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0146] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0147] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0148] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0149] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0150] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0151] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0152] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0153] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0154] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0155] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0156] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0157] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0158] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0159] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0160] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A UHV intelligent ice melting device, characterized in that: include: A first chamber, a second chamber, a third chamber, an upper outlet terminal, a lower outlet terminal, a driving mechanism, a transmission mechanism and a knife switch mechanism; The two ends of the transmission mechanism are respectively connected to the driving mechanism and the knife switch mechanism, and are used to control the contact and separation of the moving contact and the static contact in the knife switch mechanism connection; The two ends of the second chamber are respectively connected to the first chamber and the third chamber to enclose a closed internal cavity, and the knife switch mechanism, the transmission mechanism and the drive mechanism are all arranged in the internal cavity; the upper outlet terminal is fixedly arranged at the upper end of the first chamber, a conductive flange is sandwiched between the first chamber and the second chamber, and the lower outlet terminal is conductively connected to the conductive flange; The knife switch mechanism includes a moving contact, a stationary contact and a vacuum interrupter; The moving contact and the static contact are arranged oppositely in the vacuum interrupter, the vacuum interrupter is arranged inside the first chamber, the static contact is conductively connected to the upper outlet terminal, the moving contact is fixedly connected to the movable end of the transmission mechanism, and the moving contact is conductively connected to the conductive flange, so as to conductively connect the moving contact to the lower outlet terminal; The structural parameters of the moving contact, static contact and vacuum interrupter are obtained according to the following steps: According to the similarity between the installation environment vector of the setting area of ​​the ice melting device and multiple cluster environment vectors, a target correlation sequence between the structural parameters of the moving contact, the static contact and the vacuum arc extinguishing chamber in the ice melting device and the arc extinguishing time is obtained; the target correlation sequence is the correlation sequence corresponding to the cluster environment vector with the highest similarity; the correlation sequence A=(A1, A2, ..., Ai, ..., Az), wherein Ai is the correlation coefficient between the i-th structural parameter and the arc extinguishing time in the corresponding use environment; z is the total number of types of the structural parameters of the moving contact, the static contact and the vacuum arc extinguishing chamber, i=1, 2, ..., z; The installation environment vector includes the average temperature, average humidity, average ice thickness and average ice duration in the corresponding period of time when ice appears on the line in the corresponding installation area; the structural parameters of the moving contact, the static contact and the vacuum interrupter include: the volume size of the moving contact and the conductivity, melting point and erosion mass loss rate of the material, the volume size of the static contact and the conductivity, melting point and erosion mass loss rate of the material, the contact disconnection speed and the internal diameter, vacuum degree and conductivity and melting point of the shielding layer material of the vacuum interrupter; different cluster environment vectors are used to represent the installation environment vectors corresponding to different icing type environments, and the cluster environment vector and the installation environment vector include the same elements; Acquire initial parameter information, the initial parameter information including an initial structural parameter sequence and an adjustment range of each structural parameter; the initial structural parameter sequence B=(B1, B2, ..., Bi, ..., Bz), wherein Bi is an initial parameter value of the i-th structural parameter; The initial parameter information is optimized using a particle swarm optimization algorithm and a target correlation sequence to generate structural parameters of a moving contact, a static contact and a vacuum interrupter in the ice melting device used in the setting area; the absolute value of the correlation coefficient between each structural parameter in the target correlation sequence and the arc extinguishing time is used in the optimization process to adjust the self-learning coefficient of the corresponding structural parameter in the initial structural parameter sequence, and the absolute value of the correlation coefficient of the same structural parameter is positively correlated with the self-learning coefficient; The particle swarm optimization algorithm and target correlation sequence are used to optimize the initial parameter information, including: According to the target correlation sequence, the self-learning coefficient and social learning coefficient corresponding to each structural parameter in the initial structural parameter sequence are generated; wherein the self-learning coefficient c1i and social learning coefficient c2i corresponding to the i-th structural parameter in the initial structural parameter sequence meet the following conditions: ； ； Among them, |D| i is the absolute value of the correlation coefficient corresponding to the i-th structural parameter in the target correlation sequence; |D| max and |D| min are the maximum and minimum absolute values ​​of the correlation coefficient in the target correlation sequence, respectively; According to the self-learning coefficient and social learning coefficient corresponding to each structural parameter, the particle swarm optimization algorithm is used to adjust the initial value of each structural parameter in the initial structural parameter sequence.

2. The UHV intelligent ice melting device according to claim 1, characterized in that: The transmission mechanism comprises: a conductive clamp, a flexible conductive ring, a fixed pressure ring and an insulating pull rod; The fixed pressure ring is fixedly inserted into the conductive flange, and a clamping ring is arranged on the outer wall of the fixed pressure ring. One end of the conductive clamp is embraced on the outer wall of the external conductive rod of the moving contact, and the other end of the conductive clamp is clamped between the clamping ring and the conductive flange; the movable end of the insulating pull rod passes through the fixed pressure ring and is fixedly connected to the end of the external conductive rod of the moving contact; the soft-connecting conductive ring is sleeved on the insulating pull rod, and the upper and lower side walls of the soft-connecting conductive ring are respectively arranged to abut against the end of the external conductive rod of the moving contact and the upper end face of the fixed pressure ring.

3. The UHV intelligent ice melting device according to claim 2, characterized in that: The driving mechanism comprises: an electric cylinder, a first power supply and a control device; The movable end of the electric cylinder is elastically connected to the connecting end of the insulating pull rod through a spring; the control device is communicatively connected to the electric cylinder, and the first power supply is conductively connected to the control device and the electric cylinder respectively.

4. The UHV intelligent ice melting device according to claim 3, characterized in that: The driving mechanism further comprises: a mechanical power generation device and a second power source; The mechanical power generation device is conductively connected to the second power supply, and the second power supply is conductively connected to the control device and the electric cylinder respectively.

5. The UHV intelligent ice melting device according to claim 1, characterized in that: The particle swarm optimization algorithm and target correlation sequence are used to optimize the initial parameter information, including: According to the adjustment cost corresponding to each structural parameter when it is adjusted, the inertia weight corresponding to each structural parameter in the initial structural parameter sequence is generated; the adjustment cost corresponding to each structural parameter is negatively correlated with the inertia weight, and the adjustment cost includes material cost, process cost and production line adjustment cost; According to the inertia weight, self-learning coefficient and social learning coefficient corresponding to each structural parameter, the particle swarm optimization algorithm is used to adjust the initial value of each structural parameter in the initial structural parameter sequence.

6. The UHV intelligent ice melting device according to claim 5, characterized in that: The inertia weight wi corresponding to the i-th structural parameter in the initial structural parameter sequence satisfies the following conditions: ； Among them, CL i , GY i and CX i They are respectively the eigenvalue corresponding to the material cost, the eigenvalue corresponding to the process cost and the eigenvalue corresponding to the production line adjustment cost when the i-th structural parameter in the initial structural parameter sequence is adjusted.

7. The UHV intelligent ice melting device according to claim 1, characterized in that: The correlation sequence corresponding to each cluster of environment vectors is obtained according to the following steps: Clustering the installation environment vectors corresponding to multiple different areas to generate multiple environment clusters; the cluster environment vector is a center vector in the environment cluster; The area corresponding to the member of each environment cluster that is closest to the cluster environment vector is taken as the target test area; Each test ice-melting device is set in each target test area, and the average arc-extinguishing time corresponding to each test ice-melting device is obtained; the test ice-melting device has the same structure as the UHV intelligent ice-melting device; and the structural parameters of the moving contact, the static contact and the vacuum arc-extinguishing chamber in different test ice-melting devices are different; According to the structural parameter sequence and average arc extinguishing time corresponding to each test ice melting device, a correlation sequence corresponding to the structural parameters under the corresponding environment of each target test area is generated; wherein, the correlation sequence A corresponding to the structural parameters under the corresponding environment of the yth target test area is y =(A y 1. A y 2. ..., A y i, …, A y z), A y A i is the correlation coefficient between the i-th structural parameter and the arc extinguishing time in the use environment corresponding to the y-th target test area; y i satisfies the following conditions: ； Among them, d m i is the parameter value of the i-th structural parameter in the structural parameter sequence corresponding to the m-th test ice-melting device in the use environment corresponding to the y-th target test area; AVG d i mh is the average value of the i-th structural parameter in the use environment corresponding to the y-th target test area; m AVG is the average arc extinguishing time corresponding to the mth test ice melting device in the use environment corresponding to the yth target test area; mh is the mean of the average arc extinguishing time corresponding to all tested ice-melting devices in the use environment corresponding to the yth target test area; m=1, 2, …, Q, Q is the total number of tested ice-melting devices; y=1, 2, …, g, g is the total number of target test areas; The correlation sequence under the environment corresponding to each target test area is used as the correlation sequence corresponding to the cluster environment vector corresponding to each target test area.

8. The UHV intelligent ice melting device according to claim 1, characterized in that: A plurality of umbrella skirt edges are arranged at intervals on the outer side walls of the first chamber and the second chamber.

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