Intelligent adjustment device and method for storage information in the power industry

Abstract

An intelligent adjustment device and method for storage information in the electric power industry are provided. The invention comprises the following steps: obtaining all distance values, clustering all distance values ​​to obtain a plurality of distance value clusters; calculating the outlier amplitude of each distance value cluster, and obtaining a reduction distance value cluster according to the outlier amplitude; the reduction distance value cluster contains a reasonable distance value, and then only performs reduction processing on the reasonable distance value, and maintains the outlier distance value, so as to avoid the information of the outlier distance value being lost by reducing the distance value, which is not suitable for the subsequent distance analysis; calculating the reduction criticality of each reduction distance value cluster, obtaining the selection interval of each reduction distance value cluster, and obtaining the changed selection interval of each reduction distance value cluster according to the reduction criticality and the selection interval; obtaining the optimal reduction value of each reduction distance value cluster according to the changed selection interval; the optimal reduction value is the reduction value with the lowest loss, and the loss after the reduction can be reduced by reducing the distance value through the optimal reduction value.

Description

Technical Field

[0001] The present invention belongs to the technical field of storage information in the electric power industry, and in particular relates to an intelligent regulating device and method for storage information in the electric power industry. Background Art

[0002] Power equipment is essential for the normal operation of the power industry, and warehouse management directly affects the safety and reliability of the equipment. Good warehouse management can ensure that power equipment is not damaged, lost or stolen during storage, thereby ensuring the stability of power supply. In other words, the power industry involves a large number of warehousing activities, including the procurement of raw materials, storage and distribution of power equipment, etc. Optimizing warehouse management can improve operational efficiency, reduce unnecessary inventory, alleviate the financial pressure of enterprises, and meet the demand for power equipment in a timely manner, thereby improving the flexibility of the supply chain.

[0003] In terms of warehousing management and control in the electric power industry, security monitoring is particularly important. To achieve the purpose of security management and control of warehousing in the electric power industry, it is often as mentioned in the prior art solution with patent publication number "CN110286682A": An infrared obstacle avoidance sensor connected to the control unit is installed on the body of the electric power warehousing multifunctional security detection robot, which is used to sample the distance value between the obstacles in the warehouse of the electric power industry and the body of the electric power warehousing multifunctional security detection robot and transmit it to the control unit for storage. The synchronous control unit also controls the autonomous steering of the electric power warehousing multifunctional security detection robot according to the distance value.

[0004] The distance value between the obstacles in the warehouse of the power industry storage and the body of the power storage multifunctional security detection robot is sampled and transmitted to the control unit for storage. The distance value is very large. The current storage method for large distance values ​​often uses the order of the sampling time points to store all distance values ​​in the storage file, and all distance values ​​are stored. However, it requires a large storage area, consumes a lot of resources, and the mobility of retrieval will be restricted. Summary of the invention

[0005] In order to solve the defects in the prior art, the present invention proposes an intelligent adjustment device and method for storage information in the power industry, which obtains all distance values, clusters all distance values ​​to obtain several distance value clusters; calculates the outlier amplitude of each distance value cluster, and obtains the reduction distance value cluster according to the outlier amplitude; the reduction distance value cluster contains reasonable distance values, and then only performs reduction processing on the reasonable distance values, maintains the outlier distance values, and avoids the information of the outlier distance values ​​being lost by reducing the distance values, which is not suitable for the subsequent distance analysis; calculates the reduction criticality of each reduction distance value cluster, obtains the selection interval of each reduction distance value cluster, and obtains the changed selection interval of each reduction distance value cluster according to the reduction criticality and the selection interval; obtains the optimal reduction value of each reduction distance value cluster according to the changed selection interval; the optimal reduction value is the reduction value with the lowest loss, and the distance value is reduced by the optimal reduction value, which can reduce the loss after the reduction.

[0006] The present invention uses the following technical solutions.

[0007] An intelligent adjustment method for storage information in the power industry, comprising:

[0008] The infrared obstacle avoidance sensor samples the distance between the obstacles in the warehouse of the power industry and the body of the power storage multifunctional security detection robot and transmits it to the control unit for storage. The synchronous control unit also controls the autonomous steering of the power storage multifunctional security detection robot according to the distance value.

[0009] The method of transmitting the distance value to the control unit for storage includes:

[0010] Step 1, obtain distance value clustering;

[0011] Step 2, calculate the outlier amplitude of each distance value cluster, and obtain the reduced distance value cluster according to the outlier amplitude;

[0012] Step 3, calculate the reduction criticality of each reduction distance value cluster, change the selected value interval of each reduction distance value cluster according to the reduction criticality to obtain the changed selected value interval of each reduction distance value cluster, and obtain the optimal reduction value of each reduction distance value cluster according to the changed selected value interval;

[0013] Step 4: Perform reduction storage according to the optimal reduction value of each reduction distance value cluster.

[0014] Preferably, in Step 1, it is necessary to initially obtain the overall distance values ​​between the obstacles in the warehouse of the power industry storage and the body of the power storage multifunctional security detection robot transmitted under the set time conditions, use the K-Means method to process all the distance values, and form a distance value cluster from all the distance values ​​belonging to one cluster to obtain several distance value clusters.

[0015] Preferably, the duration is set to 30 seconds.

[0016] Preferably, in Step 2, the computational equation for obtaining the outlier amplitude of each distance value cluster is:

[0017] ,

[0018] In the equation, Characterization The outlier amplitude of the distance value cluster, Characterization The variance of all distance values ​​within a distance value cluster, Characterization The average of all distance values ​​within a distance value cluster, Represents the mean of all distance values ​​within the cluster. Characterization The number of distance values ​​within a distance value cluster, Characterizes the number of distance values ​​in all distance value clusters, is the Euler number.

[0019] Preferably, Step 2 specifically includes:

[0020] The outlier amplitude is lower than the pre-defined outlier amplitude threshold. The distance value clustering is treated as the decreasing distance value clustering.

[0021] Preferably, the predefined outlier amplitude threshold for .

[0022] Preferably, Step 3 specifically includes:

[0023] The calculation equation for obtaining the reduction criticality of each reduction distance value cluster according to the outlier amplitude of each reduction distance value cluster is:

[0024] ,

[0025] In the equation, Characterization The reduced criticality of the reduced distance value clusters, The minimum outlier amplitude that characterizes the clustering of all reduced distance values, The maximum value of the outlier amplitude that characterizes the clustering of all reduced distance values, Characterization The decrement distance value is the value obtained by subtracting the lowest distance value from the highest distance value in the cluster.

[0026] Preferably, Step 3 specifically further includes:

[0027] The highest distance value and the lowest distance value in each decrement distance value cluster are obtained, and the lowest distance value and the highest distance value form the selection interval of each decrement distance value cluster.

[0028] Preferably, Step 3 specifically further includes:

[0029] The equation for obtaining the changed selection interval for each reduced distance value cluster is:

[0030] ,

[0031] In the equation, Characterization The maximum limit of the selection interval of the reduced distance value cluster is The minimum distance value of the cluster with decreasing distance value, Characterization The minimum limit of the selection interval of the first decreasing distance value cluster is The highest distance value of the cluster with decreasing distance values, Characterization The maximum limit of the selected value interval after the change of the reduced distance value cluster, Characterization The minimum limit of the selected value interval after the change of the reduced distance value cluster, Characterization The changed value interval of the reduced distance value cluster.

[0032] Preferably, Step 3 specifically further includes:

[0033] The method to obtain the optimal reduction value for each reduction distance value cluster is:

[0034] For each decrement distance value cluster, a random integer value in the changed selected value interval is defined as the target value, the modulus of the amount obtained by subtracting the target value from each distance value in the decrement distance value cluster is taken as the interval amount one between each distance value and the target value, the quotient obtained by adding the total of all distance values ​​and the interval amount one of the target value divided by the variance of all distance values ​​in the distance value cluster is taken as the loss value of the target value; the integer value with the lowest loss value among all integer values ​​in the changed selected value interval is taken as the optimal decrement value of each decrement distance value cluster.

[0035] Preferably, Step 4 specifically includes:

[0036] Each decrement distance value cluster only stores the optimal decrement value, and when facing another distance value cluster, all distance values ​​in the distance value cluster must be stored.

[0037] An intelligent regulating device for storage information in the electric power industry, comprising:

[0038] An infrared obstacle avoidance sensor connected to a control unit is installed on the body of the electric power storage multifunctional security detection robot, which is used to sample the distance value between obstacles in the warehouse of the electric power industry and the body of the electric power storage multifunctional security detection robot and transmit it to the control unit for storage. The synchronous control unit also controls the autonomous steering of the electric power storage multifunctional security detection robot according to the distance value;

[0039] The interfaces running on the control unit include:

[0040] Clustering interface, which is used to obtain distance value clustering;

[0041] A calculation interface, which is used to calculate the outlier amplitude of each distance value cluster, and obtain the reduced distance value cluster according to the outlier amplitude;

[0042] The optimal interface is used to calculate the decrement criticality of each decrement distance value cluster, change the selected value interval of each decrement distance value cluster according to the decrement criticality to obtain the changed selected value interval of each decrement distance value cluster, and obtain the optimal decrement value of each decrement distance value cluster according to the changed selected value interval;

[0043] The storage interface is used to perform reduction storage according to the optimal reduction value of each reduction distance value cluster.

[0044] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0045] The whole distance value is obtained, and the whole distance value is clustered to obtain several distance value clusters; the outlier amplitude of each distance value cluster is calculated, and the reduced distance value cluster is obtained according to the outlier amplitude; the reduced distance value cluster contains reasonable distance values, and then only the reasonable distance values ​​are reduced to keep the outlier distance values, so as to avoid the information of the outlier distance values ​​being lost by the distance value reduction, which is not suitable for the subsequent distance analysis; the reduction criticality of each reduced distance value cluster is calculated, and the selection interval of each reduced distance value cluster is obtained, and the changed selection interval of each reduced distance value cluster is obtained according to the reduction criticality and the selection interval; the optimal reduction value of each reduced distance value cluster is obtained according to the changed selection interval; the optimal reduction value is the reduction value with the lowest loss, and the loss after the reduction can be reduced by reducing the distance value through the optimal reduction value. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the intelligent adjustment method of storage information in the power industry described in the present invention;

[0047] Figure 2 It is a partial structural diagram of the intelligent regulating device for storage information in the power industry described in the present invention. DETAILED DESCRIPTION

[0048] The distance value between the obstacles in the warehouse of the power industry storage and the body of the power storage multifunctional security detection robot is sampled and transmitted to the control unit for storage. The distance value is very large. The current storage method for large distance values ​​often uses the order of the sampling time points to store all distance values ​​in the storage document, and all distance values ​​are stored. However, it requires a large storage area, consumes a lot of resources, and the mobility of retrieval will be restricted. Therefore, the present invention performs reduction processing on the set distance value, improves the storage efficiency and saves the storage area without changing the change trend and properties of the distance value, so as to achieve effective control of the distance value.

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of them. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0050] like Figure 1 As shown, the intelligent adjustment method of storage information in the power industry described in the present invention includes:

[0051] The infrared obstacle avoidance sensor samples the distance value between the obstacles in the warehouse of the power industry and the body of the power storage multifunctional security detection robot and transmits it to the control unit for storage. The synchronous control unit also controls the autonomous steering of the power storage multifunctional security detection robot according to the distance value; the control unit also controls the autonomous steering of the power storage multifunctional security detection robot according to the distance value. The method in which the control unit controls the autonomous steering of the power storage multifunctional security detection robot according to the distance value can be achieved by the corresponding method mentioned in the prior art solution with patent publication number "CN110286682A".

[0052] The method of transmitting the distance value to the control unit for storage includes:

[0053] Step 1, obtain distance value clustering;

[0054] In a preferred but non-restrictive implementation of the present invention, in order to achieve the purpose of security control of power industry warehouses, the distance value between the obstacles in the power industry warehouse and the body of the power warehouse multifunctional security detection robot must be sampled and transmitted to the control unit for storage. The synchronous control unit also controls the autonomous steering of the power warehouse multifunctional security detection robot according to the distance value. Therefore, the instant sampled distance value must be stored, and a large storage area must be required to store the instant sampled distance value. To save the storage area, the sampled distance value must be reduced.

[0055] In Step 1, we first need to obtain the total distance values ​​between the obstacles in the power industry warehouse and the body of the power storage multifunctional security detection robot transmitted under the set time condition (the time length can be 30s), and use the K-Means method to process all the distance values, and form distance value clusters from all the distance values ​​belonging to one cluster to obtain several distance value clusters.

[0056] In a preferred but non-limiting embodiment of the present invention, the time length is set to 30 seconds.

[0057] Thus, several distance value clusters are obtained through the above method.

[0058] Step 2, calculate the outlier amplitude of each distance value cluster, and obtain the reduced distance value cluster according to the outlier amplitude;

[0059] When the distance value fluctuates a lot, the probability that the distance value is an outlier is also high. The outlier distance value is often the key link in analyzing the operating status of the multifunctional security detection robot for power storage, so it is not advisable to reduce the outlier distance value to avoid the loss of outlier attributes. Therefore, the distance value clusters to be reduced should be selected based on the fluctuation conditions of the data.

[0060] In a preferred but non-limiting embodiment of the present invention, in Step 2, the computational equation for obtaining the outlier amplitude of each distance value cluster is:

[0061] ,

[0062] In the equation, Characterization The outlier amplitude of the distance value cluster, Characterization The variance of all distance values ​​within a distance value cluster, Characterization The average of all distance values ​​within a distance value cluster, Represents the mean of all distance values ​​within the cluster. Characterization The number of distance values ​​within a distance value cluster, Characterizes the number of distance values ​​in all distance value clusters, is the Euler number.

[0063] It reflects the The distance amplitude between the mean distance value within a distance value cluster and the mean distance value of all distance values. The higher the distance amplitude, the higher the distance amplitude. The farther the distance value in a distance value cluster is from the overall distance value, the more likely the distance value in the distance value cluster is to be an outlier. It reflects the The distortion factor of the distance value cluster is used to measure the level of distance value fluctuation. The higher the distance value is, the higher the distance value level is within the distance value cluster, and the more dispersed the distance value is, so the distance value within the distance value cluster is more likely to be an outlier distance value. It reflects the The ratio of the number of distance values ​​within a distance value cluster to the total number of distance values. The higher the number of distance values ​​within the cluster, the higher the ratio, which indicates that the distance value within the distance value cluster is more reasonable.

[0064] In a preferred but non-limiting embodiment of the present invention, Step 2 specifically includes:

[0065] The outlier amplitude is lower than the pre-defined outlier amplitude threshold. The distance value clustering is treated as the decreasing distance value clustering.

[0066] In a preferred but non-limiting embodiment of the present invention, the predefined outlier amplitude threshold for .

[0067] Step 3, calculate the reduction criticality of each reduction distance value cluster, change the selected value interval of each reduction distance value cluster according to the reduction criticality to obtain the changed selected value interval of each reduction distance value cluster, and obtain the optimal reduction value of each reduction distance value cluster according to the changed selected value interval;

[0068] The reduced distance value clusters are obtained in the above process, and the distance values ​​in the reduced distance value clusters need to be reduced below. The difference between the distance values ​​of each distance value cluster obtained by the K-Means method is not large, and the fluctuation of the distance values ​​in the reduced distance value cluster selected by the above process is also not large, so the difference between the distance value at the center of the fluctuation of the reduced distance value cluster and other distance values ​​is not large, so the distance value at the center of the fluctuation selected in the reduced distance value cluster can be used as the reduced distance value of each reduced distance value cluster, so as to achieve distance value reduction.

[0069] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically includes:

[0070] The calculation equation for obtaining the reduction criticality of each reduction distance value cluster according to the outlier amplitude of each reduction distance value cluster is:

[0071] ,

[0072] In the equation, Characterization The reduced criticality of the reduced distance value clusters, The minimum outlier amplitude that characterizes the clustering of all reduced distance values, The maximum value of the outlier amplitude that characterizes the clustering of all reduced distance values, Characterization The amount obtained by subtracting the lowest distance value from the highest distance value in the cluster. It reflects the The matching coefficient of the outlier amplitude of the cluster with reduced distance values, The higher the value, the more dispersed the distance values ​​in the decrement distance value cluster are. Therefore, the decrement criticality of changing the decrement distance value cluster selection interval will be higher. It reflects the The matching coefficient of the fluctuation amplitude of the cluster of decrement distance values, The higher it is, the higher the error value of the distance value in the decrement distance value cluster is, and the decrement criticality of changing the decrement distance value cluster selection interval will be higher.

[0073] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically further includes:

[0074] The highest distance value and the lowest distance value in each decrement distance value cluster are obtained, and the lowest distance value and the highest distance value form the selection interval of each decrement distance value cluster.

[0075] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically further includes:

[0076] The equation for obtaining the changed selection interval for each reduced distance value cluster is:

[0077] ,

[0078] In the equation, Characterization The maximum limit of the selection interval of the reduced distance value cluster is The minimum distance value of the cluster with decreasing distance value, Characterization The minimum limit of the selection interval of the first decreasing distance value cluster is The highest distance value of the cluster with decreasing distance values, Characterization The maximum limit of the selected value interval after the change of the reduced distance value cluster, Characterization The minimum limit of the selected value interval after the change of the reduced distance value cluster, Characterization The changed selected value interval of the cluster of the decrement distance value. The higher the key degree, the lower the amplitude of the increase of the minimum limit of the selected value, the lower the amplitude of the decrease of the minimum limit of the selected value, in general, the higher the key degree, the lower the amplitude of the narrowing of the selected value interval of the decrement value, otherwise the higher the key degree. Therefore, this method can achieve the effect of obtaining a dedicated decrement value interval for a decrement range.

[0079] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically further includes:

[0080] The method to obtain the optimal reduction value for each reduction distance value cluster is:

[0081] For each decrement distance value cluster, a random integer value in the changed selected value interval is defined as the target value, the modulus of the amount obtained by subtracting the target value from each distance value in the decrement distance value cluster is taken as the interval amount one between each distance value and the target value, the quotient obtained by adding the total of all distance values ​​and the interval amount one of the target value divided by the variance of all distance values ​​in the distance value cluster is taken as the loss value of the target value; the integer value with the lowest loss value among all integer values ​​in the changed selected value interval is taken as the optimal decrement value of each decrement distance value cluster.

[0082] Step 4: Perform reduction storage according to the optimal reduction value of each reduction distance value cluster.

[0083] In a preferred but non-limiting embodiment of the present invention, Step 4 specifically includes:

[0084] Each decrement distance value cluster only stores the optimal decrement value, and when facing another distance value cluster, all distance values ​​in the distance value cluster must be stored.

[0085] like Figure 2 As shown, the intelligent adjustment device for storage information in the power industry of the present invention comprises:

[0086] An infrared obstacle avoidance sensor connected to the control unit is installed on the body of the multifunctional security detection robot for electric power storage. The infrared obstacle avoidance sensor is used to sample the distance value between the obstacles in the warehouse of the electric power industry and the body of the multifunctional security detection robot for electric power storage and transmit it to the control unit for storage. The synchronous control unit also controls the autonomous steering of the multifunctional security detection robot for electric power storage according to the distance value. The method in which the control unit also controls the autonomous steering of the multifunctional security detection robot for electric power storage according to the distance value can be achieved by the corresponding method mentioned in the prior art scheme with patent publication number "CN110286682A".

[0087] The interfaces running on the control unit include:

[0088] Clustering interface, which is used to obtain distance value clustering;

[0089] A calculation interface, which is used to calculate the outlier amplitude of each distance value cluster, and obtain the reduced distance value cluster according to the outlier amplitude;

[0090] The optimal interface is used to calculate the decrement criticality of each decrement distance value cluster, change the selected value interval of each decrement distance value cluster according to the decrement criticality to obtain the changed selected value interval of each decrement distance value cluster, and obtain the optimal decrement value of each decrement distance value cluster according to the changed selected value interval;

[0091] The storage interface is used to perform reduction storage according to the optimal reduction value of each reduction distance value cluster. The interface running on the control unit is the corresponding software or program module.

[0092] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0093] The whole distance value is obtained, and the whole distance value is clustered to obtain several distance value clusters; the outlier amplitude of each distance value cluster is calculated, and the reduced distance value cluster is obtained according to the outlier amplitude; the reduced distance value cluster contains reasonable distance values, and then only the reasonable distance values ​​are reduced to keep the outlier distance values, so as to avoid the information of the outlier distance values ​​being lost by the distance value reduction, which is not suitable for the subsequent distance analysis; the reduction criticality of each reduced distance value cluster is calculated, and the selection interval of each reduced distance value cluster is obtained, and the changed selection interval of each reduced distance value cluster is obtained according to the reduction criticality and the selection interval; the optimal reduction value of each reduced distance value cluster is obtained according to the changed selection interval; the optimal reduction value is the reduction value with the lowest loss, and the loss after the reduction can be reduced by reducing the distance value through the optimal reduction value.

[0094] The present disclosure can be a system, method and / or computer program product. The computer program product can include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to achieve various aspects of the present disclosure.

[0095] A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (such as, a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0096] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0097] The computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on the controller computer, partially on the controller computer, as a separate software package, partially on the controller computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the controller computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (just as using an Internet service provider to connect through the Internet). In some embodiments, the electronic circuit is customized by using state information of computer-readable program instructions, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), which can execute computer-readable program instructions to achieve various aspects of the present disclosure.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent adjustment method for storage information in the power industry, characterized in that: include: The infrared obstacle avoidance sensor samples the distance between the obstacles in the warehouse of the power industry and the body of the power storage multifunctional security detection robot and transmits it to the control unit for storage. The synchronous control unit also controls the autonomous steering of the power storage multifunctional security detection robot according to the distance value. The method of transmitting the distance value to the control unit for storage includes: Step 1, obtain distance value clustering; Step 2, calculate the outlier amplitude of each distance value cluster, and obtain the reduced distance value cluster according to the outlier amplitude; Step 3, calculate the reduction criticality of each reduction distance value cluster, change the selected value interval of each reduction distance value cluster according to the reduction criticality to obtain the changed selected value interval of each reduction distance value cluster, and obtain the optimal reduction value of each reduction distance value cluster according to the changed selected value interval; Step 4, perform reduction storage according to the optimal reduction value of each reduction distance value cluster; In Step 1, the total distance values ​​between the obstacles in the power industry warehouse and the body of the power storage multifunctional security detection robot transmitted under the set time condition are obtained, and the K-Means method is used to process the total distance values, and the total distance values ​​belonging to one cluster are formed into distance value clusters to obtain several distance value clusters; Set the duration to 30s; In Step 2, the calculation equation for obtaining the outlier amplitude of each distance value cluster is: In the equation, Characterization The outlier amplitude of the distance value cluster, Characterization The variance of all distance values ​​within a distance value cluster, Characterization The average of all distance values ​​within a distance value cluster, Represents the mean of all distance values ​​within the cluster. Characterization The number of distance values ​​within a distance value cluster, Characterizes the number of distance values ​​in all distance value clusters, is the Euler number; Step 3 specifically includes: The calculation equation for obtaining the reduction criticality of each reduction distance value cluster according to the outlier amplitude of each reduction distance value cluster is: In the equation, Characterization The reduced criticality of the reduced distance value clusters, The minimum outlier amplitude that characterizes the clustering of all reduced distance values, The maximum value of the outlier amplitude that characterizes the clustering of all reduced distance values, Characterization The amount obtained by subtracting the lowest distance value from the highest distance value in the cluster; Step 3 specifically includes: Obtain the highest distance value and the lowest distance value in each decrement distance value cluster, and use the lowest distance value and the highest distance value to form a selection interval for each decrement distance value cluster; Step 3 specifically includes: The equation for obtaining the changed selection interval for each reduced distance value cluster is: In the equation, Characterization The maximum limit of the selection interval of the reduced distance value cluster is The minimum distance value of the cluster with decreasing distance value, Characterization The minimum limit of the selection interval of the first decreasing distance value cluster is The highest distance value of the cluster with decreasing distance values, Characterization The maximum limit of the selected value interval after the change of the reduced distance value cluster, Characterization The minimum limit of the selected value interval after the change of the reduced distance value cluster, Characterization The changed selected value interval of the reduced distance value cluster; Step 3 specifically includes: The method to obtain the optimal reduction value for each reduction distance value cluster is: For each decrement distance value cluster, a random integer value in the changed selected value interval is defined as the target value, the modulus of the amount obtained by subtracting the target value from each distance value in the decrement distance value cluster is taken as the interval amount one between each distance value and the target value, the quotient obtained by adding the total of all distance values ​​and the interval amount one of the target value divided by the variance of all distance values ​​in the distance value cluster is taken as the loss value of the target value; the integer value with the lowest loss value among all integer values ​​in the changed selected value interval is taken as the optimal decrement value of each decrement distance value cluster.

2. The intelligent adjustment method for storage information in the power industry according to claim 1 is characterized in that: Step 2 specifically includes: The outlier amplitude is lower than the pre-defined outlier amplitude threshold. The distance value clustering is treated as the reduced distance value clustering; Predefined outlier magnitude threshold for .

3. The intelligent adjustment method for storage information in the power industry according to claim 2 is characterized in that: Step 4 specifically includes: Each decrement distance value cluster only stores the optimal decrement value, and when facing another distance value cluster, all distance values ​​in the distance value cluster must be stored.

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