An intelligent power distribution cabinet anti-theft system for big data processing

Abstract

The present invention relates to the technical field of power distribution cabinets, and discloses an intelligent power distribution cabinet anti-theft system for big data processing, comprising: a power distribution cabinet device, comprising a true power distribution layer, a pseudo power distribution layer and an identification module; wherein the identification module comprises an active unit and a passive unit, the active unit is a key of a characteristic structure, the passive unit is a magnetic induction measuring instrument, and the passive unit is used to obtain a characteristic cutting signal of the active unit; wherein the true power distribution layer is used to realize regional current distribution, and the pseudo power distribution layer comprises a plurality of positioning units; an unlocking module, within a target time period, unlocks the true power distribution layer or the pseudo power distribution layer based on the characteristic cutting signal; if the pseudo power distribution layer is unlocked, a preset alarm unit is activated to alarm a power distribution cabinet manager; a positioning module, used to obtain positioning information of multiple positioning units at fixed time intervals, fuse the multiple positioning information, obtain fused positioning, and send the fused positioning to the power distribution cabinet manager.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution cabinets, and more specifically, to an intelligent power distribution cabinet anti-theft system for big data processing. Background Art

[0002] As a key node in power distribution, the security of the distribution cabinet directly affects the stability of regional power supply and the quality of life of residents. However, there are economically valuable devices in the distribution cabinet, and the existing anti-theft measures are usually based on 24-hour monitoring or strict identity verification to protect the distribution cabinet from theft. However, in this way, criminals can easily escape by covering their body features, and the distribution cabinet will be violently unlocked, causing regional power system failures. Summary of the invention

[0003] The present invention provides an intelligent power distribution cabinet anti-theft system for big data processing, which solves the technical problems raised in the background technology.

[0004] The present invention provides an intelligent power distribution cabinet anti-theft system for big data processing, comprising:

[0005] A power distribution cabinet device, including a true power distribution layer, a pseudo power distribution layer and an identification module;

[0006] The identification module includes an active unit and a passive unit. The active unit is a key with a characteristic structure, and the passive unit is a magnetic induction measuring instrument. The passive unit is used to obtain a characteristic cutting signal of the active unit.

[0007] The real distribution layer is used to realize regional current distribution, and the pseudo distribution layer includes several positioning units, and the positioning units in the pseudo distribution layer are hidden based on the internal structure of the real distribution layer;

[0008] The unlocking module unlocks the real distribution layer or the pseudo distribution layer based on the characteristic cutting signal within the target time period; if the pseudo distribution layer is unlocked, the preset alarm unit is activated to alarm the distribution cabinet manager;

[0009] The positioning module is used to obtain the positioning information of multiple positioning units at fixed time intervals, fuse the multiple positioning information, obtain the fused positioning, and send the fused positioning to the distribution cabinet manager.

[0010] Furthermore, the key of the characteristic structure is composed of magnetic material and non-magnetic material at intervals, including:

[0011] Perform real number encoding on N legal users respectively to obtain the unlocking vector of each legal user;

[0012] Randomly combine magnetic materials and non-magnetic materials to obtain N types of keys. Each legitimate user is assigned a key. The random combinations include:

[0013] Get the standard size and shape of the key and divide it into K sub-segments at fixed distance intervals;

[0014] Randomly fill the k-th sub-segment with magnetic material or non-magnetic material to obtain a key filling vector; wherein the dimension of the key filling vector is K, and the k-th element of the key filling vector is composed of 0 and 1, 0 represents magnetic material, 1 represents non-magnetic material, and 1≤k≤K;

[0015] The Euclidean distance between the key filling vectors corresponding to any two keys is greater than a preset distance threshold.

[0016] Furthermore, the characteristic cutting signal includes:

[0017] For the i-th key, a time-voltage signal is obtained based on a passive unit;

[0018] The time-voltage signal is time-normalized to obtain the characteristic cutting signal of the i-th key.

[0019] Furthermore, the positioning unit in the pseudo distribution layer is hidden based on the internal structure of the true distribution layer, specifically: the external structure and layout of the current distribution device inside the true distribution layer are obtained, and the current distribution device includes but is not limited to: voltage transformers and current transformers; based on the external structure and layout of the current distribution device, the pseudo distribution layer is configured with a device and layout with the same external structure as the current distribution device; wherein the positioning unit is hidden inside a device with the same external structure as the current distribution device.

[0020] Furthermore, unlocking the real power distribution layer or the pseudo power distribution layer includes:

[0021] In a target time period, a target time-voltage signal is acquired based on a passive unit, and a time normalization process is performed on the target time-voltage signal to obtain a target cutting signal;

[0022] Perform similarity matching between the target cutting signal and N characteristic cutting signals respectively;

[0023] If the match is successful, the real power distribution layer will be unlocked;

[0024] If the matching fails, the fake distribution layer is unlocked and the alarm unit is activated.

[0025] Furthermore, similarity matching includes: calculating the DTW distance between the target cutting signal and the i-th feature cutting signal based on the DTW algorithm, and taking the DTW distance as the corresponding similarity; if the similarity is less than or equal to a preset threshold, the matching is successful; if the similarity is greater than the preset threshold, the matching fails.

[0026] Furthermore, the positioning unit is constructed based on the GNSS module.

[0027] Furthermore, multiple positioning information is fused, including: obtaining multiple NMEA data based on multiple positioning units; the NMEA data includes: longitude, latitude and altitude parameters; and fusing the NMEA data according to a fusion algorithm to obtain a fused positioning; wherein the fusion algorithm includes but is not limited to: a Kalman filter algorithm and a particle filter algorithm.

[0028] The beneficial effects of the present invention are as follows: by separating the real distribution layer from the pseudo distribution layer, combining a key with magnetic induction characteristics, time normalization matching, and data fusion of multiple positioning units, it is possible to accurately trap, alarm in real time, and locate illegal intrusions with high precision while ensuring the continuity of regional power supply, thereby significantly improving the safety and anti-theft efficiency of the distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a module diagram of an intelligent power distribution cabinet anti-theft system for big data processing of the present invention;

[0030] Figure 2 is a schematic diagram of a power distribution cabinet device of the present invention;

[0031] Figure 3 It is a schematic diagram of a key having a characteristic structure of the present invention. DETAILED DESCRIPTION

[0032] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0033] like Figure 1 to Figure 3 As shown, an intelligent power distribution cabinet anti-theft system for big data processing includes:

[0034] A power distribution cabinet device, including a true power distribution layer, a pseudo power distribution layer and an identification module;

[0035] The identification module includes an active unit and a passive unit. The active unit is a key with a characteristic structure, and the passive unit is a magnetic induction measuring instrument. The passive unit is used to obtain a characteristic cutting signal of the active unit.

[0036] The real distribution layer is used to realize regional current distribution, and the pseudo distribution layer includes several positioning units, and the positioning units in the pseudo distribution layer are hidden based on the internal structure of the real distribution layer;

[0037] The unlocking module unlocks the real distribution layer or the pseudo distribution layer based on the characteristic cutting signal within the target time period; if the pseudo distribution layer is unlocked, the preset alarm unit is activated to alarm the distribution cabinet manager;

[0038] The positioning module is used to obtain the positioning information of multiple positioning units at fixed time intervals, fuse the multiple positioning information, obtain the fused positioning, and send the fused positioning to the distribution cabinet manager.

[0039] For example, a set of intelligent power distribution cabinet anti-theft system of the present invention is deployed in a certain industrial park to ensure continuous power supply to factories and residents in the area. In practical applications, the power distribution cabinet device is divided into a true distribution layer and a pseudo distribution layer, wherein the true distribution layer is responsible for actual current distribution, while the pseudo distribution layer is embedded with multiple GNSS positioning units and disguised by an appearance structure similar to the true distribution layer. When legitimate maintenance personnel perform regular maintenance, they will use a pre-customized characteristic structure key, which consists of multiple sub-segments with fixed intervals, and each sub-segment is randomly filled with magnetic or non-magnetic materials to form a unique key filling vector. When the key is inserted, the magnetic induction meter in the system detects the time-voltage signal generated by the key cutting the magnetic flux lines in a pre-established stable magnetic field, and performs time normalization on the signal. Subsequently, the DTW algorithm is used to match the obtained target cutting signal with the stored legitimate characteristic cutting signal for similarity, ensuring accurate identification even if there is a slight difference in the insertion speed. If the match is successful, the system unlocks the real distribution layer and allows normal maintenance; if the match fails, the system unlocks the fake distribution layer and immediately activates the alarm device. When the criminals steal the fake distribution layer, the GNSS positioning unit collects positioning data at fixed time intervals, and after fusion by the Kalman filter algorithm, generates accurate fusion positioning information and transmits it to the distribution cabinet management center in real time. In this way, even if illegal operations occur, the power supply will not be interrupted, and the intruder can be quickly captured and located, realizing intelligent and all-round protection of the distribution cabinet security.

[0040] In one embodiment of the present invention, the key of the characteristic structure is composed of magnetic material and non-magnetic material, and includes:

[0041] Perform real number encoding on N legal users respectively to obtain the unlocking vector of each legal user;

[0042] Randomly combine magnetic materials and non-magnetic materials to obtain N types of keys. Each legitimate user is assigned a key. The random combinations include:

[0043] Get the standard size and shape of the key and divide it into K sub-segments at fixed distance intervals;

[0044] Randomly fill the k-th sub-segment with magnetic material or non-magnetic material to obtain a key filling vector; wherein the dimension of the key filling vector is K, and the k-th element of the key filling vector is composed of 0 and 1, 0 represents magnetic material, 1 represents non-magnetic material, and 1≤k≤K;

[0045] The Euclidean distance between the key filling vectors corresponding to any two keys is greater than a preset distance threshold.

[0046] Specifically, the key is composed of alternating magnetic and non-magnetic materials, and the different performances of these two materials in the magnetic field are used to generate a unique magnetic induction signal. For the N legitimate users in the system, an "unlock vector" is generated for each user through real number encoding, which means that the key corresponding to each user has a unique digital identifier. The physical shape of the key is specified as a standard size and shape, and is divided into K sub-segments within a fixed distance interval. It should be noted that the physical shape of the key is not special and conforms to the shape and size of common keys. In each sub-segment, magnetic materials or non-magnetic materials are randomly filled to form a "key filling vector" composed of 0 and 1. Here, 0 indicates that the sub-segment is filled with magnetic materials, and 1 indicates non-magnetic materials. This method ensures that each key has a K-dimensional binary vector as the code. The design requires that the Euclidean distance between the filling vectors of any two keys must be greater than the preset threshold, so as to ensure that the keys of different users are sufficiently different in coding to prevent mismatching.

[0047] In one embodiment of the present invention, the characteristic cutting signal includes:

[0048] For the i-th key, a time-voltage signal is obtained based on a passive unit;

[0049] The time-voltage signal is time-normalized to obtain the characteristic cutting signal of the i-th key.

[0050] Specifically, for the i-th key, a passive unit (magnetic induction meter) is used to detect the voltage signal generated in the magnetic field due to the metal structure of the key "cutting" the magnetic flux lines when the key is inserted. This signal changes with time, forming a time-voltage signal. Since the insertion speed of the key may be inconsistent in actual operation, directly comparing the original time-voltage signal will be affected by the difference in time scale. Therefore, the signal is time-normalized to adjust the time axis of the signal to a unified standard so that the key features in the signal do not change due to changes in insertion speed. After normalization, the obtained time-voltage signal becomes the "feature cutting signal" of the i-th key. This signal is unique and can be used for subsequent similarity matching and identity verification.

[0051] In one embodiment of the present invention, the positioning unit in the pseudo distribution layer is hidden based on the internal structure of the true distribution layer, specifically: the outer structure and layout of the current distribution device inside the true distribution layer are obtained, and the current distribution device includes but is not limited to: voltage transformers and current transformers; based on the outer structure and layout of the current distribution device, the pseudo distribution layer is configured with a device and layout with the same outer structure as the current distribution device; wherein the positioning unit is hidden inside a device with the same outer structure as the current distribution device.

[0052] Specifically, the real distribution layer is usually equipped with voltage transformers, current transformers and other devices for current distribution. The appearance, structure and layout of these devices are fixed and obvious in the distribution cabinet. Taking advantage of this, the present invention configures devices with the same appearance, structure and layout as those in the real distribution layer in the pseudo distribution layer. These devices do not actually participate in power supply, but are specifically used to hide the positioning unit. The positioning unit is placed inside these structures disguised as current distribution devices, so that the appearance is consistent with the devices in the real distribution layer, achieving a camouflage effect. In this way, illegal intruders can steal the disguised positioning unit after unlocking the pseudo distribution layer.

[0053] In one embodiment of the present invention, unlocking the real power distribution layer or the pseudo power distribution layer includes:

[0054] In a target time period, a target time-voltage signal is acquired based on a passive unit, and a time normalization process is performed on the target time-voltage signal to obtain a target cutting signal;

[0055] Perform similarity matching between the target cutting signal and N characteristic cutting signals respectively;

[0056] If the match is successful, the real power distribution layer will be unlocked;

[0057] If the matching fails, the fake distribution layer is unlocked and the alarm unit is activated.

[0058] Specifically, within the predetermined target time period, the passive unit (magnetic induction meter) will collect the time-voltage signal generated by the current operation. In order to eliminate the time scale difference caused by factors such as insertion speed, the signal is time-normalized to obtain a standardized "target cutting signal". The system matches the target cutting signal with the "feature cutting signals" corresponding to the N pre-stored legal keys for similarity. The matching result can reflect the similarity between the target signal and each legal signal. If the similarity between the target signal and a certain feature cutting signal is lower than the preset threshold, it is considered that the match is successful. At this time, it proves that the operation is using a legal key. The system will unlock the real distribution layer and allow normal power supply and maintenance operations. On the contrary, if the similarity between the target signal and all feature cutting signals does not meet the requirements, the operation is considered illegal. The system will unlock the pseudo distribution layer and activate the alarm unit to notify the distribution cabinet manager.

[0059] In one embodiment of the present invention, similarity matching includes: calculating the DTW distance between the target cutting signal and the i-th feature cutting signal based on the DTW algorithm, and taking the DTW distance as the corresponding similarity; if the similarity is less than or equal to a preset threshold, the matching is successful; if the similarity is greater than the preset threshold, the matching fails.

[0060] Specifically, the DTW (Dynamic Time Warping) algorithm is used to calculate the DTW distance between the target cutting signal and the i-th characteristic cutting signal. The DTW algorithm can handle the nonlinear alignment problem of signals on the local time scale, ensuring that even if there is a slight time offset in actual operation, the similarity of the two signals can still be accurately measured. The calculated DTW distance is used as a similarity indicator. If the DTW distance is less than or equal to the preset threshold, it is considered that the target cutting signal and the characteristic cutting signal are matched successfully, indicating that a legal key is used; conversely, if the DTW distance is greater than the preset threshold, the match fails, indicating that the operation is illegal.

[0061] In one embodiment of the present invention, the positioning unit is constructed based on a GNSS module.

[0062] Specifically, the positioning unit is implemented based on the GNSS module. That is to say, the present invention uses the GNSS (such as GPS, Beidou and other satellite navigation systems) module to obtain positioning information (such as longitude, latitude and altitude), so as to provide reliable raw data for subsequent positioning data fusion. The advantage of using the GNSS module is that it can provide higher positioning accuracy outdoors or in an environment with good signal conditions, which is convenient for real-time tracking of the location information of the distribution cabinet during abnormal operation, and then quickly notifying the management personnel for processing.

[0063] In one embodiment of the present invention, multiple positioning information are fused, including: acquiring multiple NMEA data based on multiple positioning units; the NMEA data includes: longitude, latitude and altitude parameters; and fusing the NMEA data according to a fusion algorithm to obtain a fused positioning; wherein the fusion algorithm includes but is not limited to: a Kalman filter algorithm and a particle filter algorithm.

[0064] Specifically, multiple positioning units each obtain positioning data through the GNSS module. These data are usually output in NMEA format, including basic parameters such as longitude, latitude and altitude. The data of a single positioning unit may be affected by environmental interference, noise or signal loss, and direct use is prone to positioning errors. By fusing the NMEA data of multiple positioning units, the errors of each other can be compensated to improve the accuracy and robustness of positioning. The fusion algorithm takes multiple NMEA data as input, and weights, smoothes and predicts different data through algorithms (such as Kalman filter algorithm or particle filter algorithm) to obtain an optimal fusion positioning result. After fusion processing, the system can obtain a more stable and accurate fusion positioning information, which will be sent to the distribution cabinet manager to locate and track abnormal situations in a timely manner.

[0065] In one embodiment of the present invention, standard NMEA data is extracted for the position of the power distribution cabinet, and then in the subsequent NMEA data fusion process, if standard NMEA data exists, the standard NMEA data is removed, and then the remaining NMEA data is fused. It should be noted that fusing NMEA data based on a Kalman filter algorithm or a particle filter algorithm is a common technical means, so it will not be repeated.

[0066] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. An intelligent power distribution cabinet anti-theft system for big data processing, characterized in that: include: A power distribution cabinet device, including a true power distribution layer, a pseudo power distribution layer and an identification module; The identification module includes an active unit and a passive unit. The active unit is a key with a characteristic structure, and the passive unit is a magnetic induction measuring instrument. The passive unit is used to obtain a characteristic cutting signal of the active unit. The real distribution layer is used to realize regional current distribution, and the pseudo distribution layer includes several positioning units, and the positioning units in the pseudo distribution layer are hidden based on the internal structure of the real distribution layer; The unlocking module unlocks the real distribution layer or the pseudo distribution layer based on the characteristic cutting signal within the target time period; if the pseudo distribution layer is unlocked, the preset alarm unit is activated to alarm the distribution cabinet manager; The positioning module is used to obtain the positioning information of multiple positioning units at fixed time intervals, fuse the multiple positioning information, obtain the fused positioning, and send the fused positioning to the distribution cabinet manager; the key of the characteristic structure is composed of magnetic material and non-magnetic material intervals, including: Perform real number encoding on N legal users respectively to obtain the unlocking vector of each legal user; Randomly combine magnetic materials and non-magnetic materials to obtain N types of keys. Each legitimate user is assigned a key. The random combinations include: Get the standard size and shape of the key and divide it into K sub-segments at fixed distance intervals; Randomly fill the k-th sub-segment with magnetic material or non-magnetic material to obtain a key filling vector; wherein the dimension of the key filling vector is K, and the k-th element of the key filling vector is composed of 0 and 1, 0 represents magnetic material, 1 represents non-magnetic material, and 1≤k≤K; The Euclidean distance of the key filling vectors corresponding to any two keys is greater than a preset distance threshold; unlocking the true distribution layer or the pseudo distribution layer, including: within the target time period, obtaining the target time-voltage signal based on the passive unit, and time-normalizing the target time-voltage signal to obtain the target cutting signal; performing similarity matching on the target cutting signal with N characteristic cutting signals respectively; if the match is successful, unlocking the true distribution layer based on the corresponding key filling vector; if the match fails, unlocking the pseudo distribution layer and activating the alarm unit; similarity matching, including: calculating the DTW distance between the target cutting signal and the i-th characteristic cutting signal based on the DTW algorithm, and taking the DTW distance as the corresponding similarity; if the similarity is less than or equal to the preset threshold, the match is successful; if the similarity is greater than the preset threshold, the match fails.

2. The intelligent power distribution cabinet anti-theft system for big data processing according to claim 1 is characterized in that: Characteristic cutting signals, including: For the i-th key, a time-voltage signal is obtained based on a passive unit; The time-voltage signal is time-normalized to obtain the characteristic cutting signal of the i-th key.

3. The intelligent power distribution cabinet anti-theft system for big data processing according to claim 1 is characterized in that: The positioning unit in the pseudo distribution layer is hidden based on the internal structure of the true distribution layer, specifically: the external structure and layout of the current distribution device inside the true distribution layer are obtained, and the current distribution device includes but is not limited to: voltage transformer and current transformer; based on the external structure and layout of the current distribution device, the pseudo distribution layer is configured with a device and layout with the same external structure as the current distribution device; wherein the positioning unit is hidden inside a device with the same external structure as the current distribution device.

4. The intelligent power distribution cabinet anti-theft system for big data processing according to claim 1 is characterized in that: The positioning unit is built based on the GNSS module.

5. The intelligent power distribution cabinet anti-theft system for big data processing according to claim 1 is characterized in that: Fusion of multiple positioning information includes: acquiring multiple NMEA data based on multiple positioning units; the NMEA data includes: longitude, latitude and altitude parameters; and fusing the NMEA data according to a fusion algorithm to obtain fused positioning; wherein the fusion algorithm includes but is not limited to: a Kalman filter algorithm and a particle filter algorithm.

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