Substation personnel authority management and lock state monitoring method

Abstract

The invention discloses a transformer station personnel authority management and lock state monitoring method, and relates to the technical field of identity verification, and the method comprises the steps: collecting the fingerprint information and identity information of a transformer station worker, building a corresponding relation between the fingerprint information and the identity information, and storing the corresponding relation into an authority database; when a worker performs fingerprint verification, a fingerprint acquisition module of the intelligent lock control unit acquires on-site fingerprint information of the worker, and sends the on-site fingerprint information to an authority database for comparison through a wireless communication module; and an unlocking instruction or a rejection instruction is sent to the intelligent lock control unit according to a comparison result, a lock state log is generated according to the acquisition time, the acquisition place and the unlocking state, and the lock state log is displayed on a display terminal of a transformer substation monitoring center. By integrating the fingerprint identification technology, the intelligent lock control unit and the wireless communication module, accurate authority management of substation personnel and real-time monitoring of the lock state are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of identity authentication, and in particular to a method for personnel authority management and lock status monitoring in a transformer substation. Background Art

[0002] With the continuous development of modern power systems, the operation and maintenance management of substations has gradually become intelligent. Due to the large number of equipment and complex areas in substations, the rights of staff entering and leaving substations are usually strictly managed for safety and management needs. Traditional personnel authority management methods mostly rely on manual records or card identity authentication, which has problems such as incomplete records and lax identity authentication, and it is difficult to meet the needs of efficient and safe management. In recent years, biometric technology, especially fingerprint recognition technology, has gradually been applied to personnel management of power facilities due to its advantages in identity uniqueness and convenience. However, fingerprint recognition technology is still faced with technical bottlenecks such as poor data real-time performance and unstable communication transmission in substation applications, which limits its actual application effect. Based on this, studying an intelligent system that can both ensure accurate management of personnel rights and realize lock status monitoring has become an important topic to improve the management level of substations.

[0003] Existing substation management systems mostly use fingerprint or card verification to manage permissions, and some systems use smart locks to achieve local security control, but these systems still have obvious deficiencies in multi-scenario applications, flexibility of permission management, and integrity of log records. First, existing technologies mostly rely on a single identity authentication method, which cannot strike a balance between the accuracy and timeliness of permission matching, resulting in errors in permission authentication results. Secondly, the status records of smart locks are not detailed enough, and often lack tracking of key data such as unlocking time, location, and unlocking status, which reduces the traceability of substation security incidents. In addition, due to the limitations of permission data storage and communication modules, the real-time performance of existing systems is poor and cannot meet the dynamic management needs of multi-level and multi-scenario substations. It can be seen that in response to the needs of permission management and lock status monitoring, the existing technologies need to be improved in terms of management accuracy, data integrity, and system timeliness. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention proposes a method for substation personnel authority management and lock status monitoring.

[0005] Therefore, the present invention provides a method for substation personnel authority management and lock status monitoring, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for substation personnel authority management and lock status monitoring, which includes:

[0008] Collect fingerprint information and identity information of substation staff, establish a corresponding relationship between the fingerprint information and the identity information and store them in the authority database;

[0009] When the staff member performs fingerprint verification, the fingerprint collection module of the intelligent lock control unit collects the staff member's on-site fingerprint information, and sends the on-site fingerprint information to the authority database for comparison through the wireless communication module;

[0010] Sending an unlocking command or a rejecting command to the intelligent lock control unit according to the comparison result, and recording the collection time, collection location and unlocking status of the on-site fingerprint information;

[0011] The collection time, collection location and unlocking status are used to generate a lock status log, and the lock status log is displayed on a display terminal in a substation monitoring center.

[0012] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, wherein: the identity information includes the name, work number, department, job level and contact information of the staff member;

[0013] The fingerprint information is stored in binary form, and a one-to-one mapping table is established with the identity information; a unique identity code is assigned to each staff member in the mapping table;

[0014] According to the job level of the staff, different access control authority levels are set in the mapping table. The access control authority levels are divided into Class A, Class B and Class C, where Class A has access to all areas, Class B has access to important areas, and Class C only has access to ordinary areas.

[0015] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, wherein: establishing a corresponding relationship between the fingerprint information and the identity information and storing them in the authority database includes:

[0016] Converting the fingerprint feature data of the staff member into a feature point coordinate sequence, wherein the feature point coordinate sequence is composed of the spatial position information of the end points, bifurcation points, and arc points of the fingerprint pattern;

[0017] Digitally encoding the feature point coordinate sequence to generate a 128-bit fingerprint feature code;

[0018] Encrypting the fingerprint feature code and identity information in the mapping table, and storing the encrypted data in the authority database;

[0019] The encryption can be expressed as follows:

[0020] C=SM4(K,P⊕T)⊕H(R||S)

[0021] Where C is the ciphertext output, K is the 16-byte encryption key, P is the plaintext data, T is the timestamp confusion factor, R is the random number sequence, S is the session identifier, H is the SM3 hash function, ⊕ represents the XOR operation, and || represents the concatenation operation;

[0022] The calculation of the timestamp confusion factor T can be expressed by the following formula:

[0023] T=(t×α1+β)mod2 128

[0024] Where t is the current UNIX timestamp, α1 is the prime multiplier, and β is the random offset.

[0025] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, wherein: the fingerprint information includes grayscale data of the fingerprint image, feature point distribution data and fingerprint pattern direction data;

[0026] The fingerprint collection module pre-processes the collected fingerprint image;

[0027] Extract feature point data from the preprocessed fingerprint image and generate a feature point vector set;

[0028] The feature point data includes endpoint features, bifurcation point features and singular point features.

[0029] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, the preprocessing includes image enhancement, which can be expressed by the following formula:

[0030] L(x,y)=α·H(x,y)+(1-α)·G(x,y)

[0031] Among them, L(x,y) is the enhanced image, H(x,y) is the histogram equalization result, G(x,y) is the Gaussian filtering result, and α is the adaptive weight coefficient;

[0032] The calculation of the adaptive weight coefficient α can be expressed by the following formula:

[0033]

[0034] Among them, σ 2 is the local area variance, k is the adjustment factor, and ω(x,y) is the local texture complexity;

[0035] The calculation of the texture complexity ω(x, y) can be expressed by the following formula:

[0036]

[0037] in, is the image gradient, M and N are the local window sizes, and V is the normalization factor.

[0038] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, the feature point vector set also includes a weight coefficient of each feature point, which is calculated by the following formula:

[0039] W(p i )=μ·R(p i )+λ·Q(p i )

[0040] Among them, R(p i ) is the reliability of the characteristic point, Q(p i ) is the quality score, μ and λ are weight factors;

[0041] The calculation of the reliability of the feature point can be expressed by the following formula:

[0042]

[0043] Among them, d is the distance from the feature point to the nearest singular point, and D is the feature point density parameter.

[0044] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, wherein: the comparison result is determined according to the similarity score;

[0045] The similarity score is calculated as follows:

[0046]

[0047] Among them, M is the matching degree of feature points, N is the consistency of texture direction, O is the similarity of core point area, and β1, β2, and β3 are corresponding weight coefficients;

[0048] The feature point matching degree can be expressed by the following formula:

[0049]

[0050] Among them, θ i is the direction difference angle of the i-th pair of matching feature points, W(p i ) is the feature point weight, d i is the spatial distance of feature points, and D is the distance normalization parameter.

[0051] As a preferred solution of the method for substation personnel authority management and lock status monitoring of the present invention, when the similarity score is greater than a first preset threshold, the verification time is within the authorized period of the staff, and the staff status is on duty and the authority is not restricted, an unlocking instruction is generated, and the unlocking instruction includes: a device identification code, an authorization code, an unlocking time limit and an operation sequence number;

[0052] When the similarity score is within the preset range or the verification time exceeds the authorization period ±15 minutes, a first rejection instruction is generated; when the similarity score is less than the second preset threshold, or the verification time exceeds the authorization period ±60 minutes, or the number of repeated verification failures exceeds 3 times in a short period of time, a second rejection instruction is generated, and the second rejection instruction includes: device identification code, rejection reason code, operation sequence number and warning level.

[0053] In a second aspect, an embodiment of the present invention provides a substation personnel authority management and lock status monitoring system, which includes:

[0054] A data collection module is used to collect fingerprint information and identity information of substation staff, establish a corresponding relationship between the fingerprint information and the identity information, and store them in the authority database;

[0055] A comparison module, used to send the on-site fingerprint information to the authority database for comparison through the wireless communication module;

[0056] An instruction sending module, used to send an unlocking instruction or a rejecting instruction to the intelligent lock control unit according to the comparison result, and record the collection time, collection location and unlocking status of the on-site fingerprint information;

[0057] The log generation module is used to generate a lock status log based on the collection time, collection location and unlocking status, and display the lock status log on a display terminal of the substation monitoring center.

[0058] The beneficial effect of the present invention is that by integrating fingerprint recognition technology, intelligent lock control unit and wireless communication module, accurate authority management of substation personnel and real-time monitoring of lock status are realized. Specifically, the system can establish a correspondence between the identity information and fingerprint characteristics of the staff, set hierarchical permissions, and verify access permissions through smart locks to ensure that only authorized personnel can access specific areas. At the same time, the system records detailed verification event information (such as fingerprint data, collection time and location, unlocking status, etc.), which improves security and transparency. Through synchronous backup of the cloud and local servers, the reliability and real-time update of data are ensured, effectively improving the intelligence and automation level of substation safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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. Among them:

[0060] Figure 1 The present invention is a flow chart of a method for substation personnel authority management and lock status monitoring. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0065] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] Example 1

[0068] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for substation personnel authority management and lock status monitoring, including:

[0069] S1: Collect fingerprint information and identity information of substation staff, establish a corresponding relationship between the fingerprint information and the identity information, and store them in the authority database;

[0070] Collect fingerprint information and identity information of substation staff, where the identity information includes the staff's name, work number, department, job level and contact information; obtain fingerprint feature data of the thumbs and index fingers of both hands of the staff through a fingerprint collector, and the fingerprint feature data is stored in binary form; establish a one-to-one mapping table between the fingerprint feature data and the identity information, and assign a unique identity code to each staff member in the mapping table; set different access control authority levels in the mapping table according to the staff's job level, and the access control authority levels are divided into Class A, Class B and Class C, where Class A has access to all areas, Class B has access to some important areas, and Class C only has access to ordinary areas; write the mapping table and authority level information into the authority database, which adopts a distributed storage structure and is synchronously backed up on the substation local server and the cloud server.

[0071] It should be noted that establishing a one-to-one mapping table between the fingerprint feature data and the identity information specifically includes:

[0072] The fingerprint feature data of the staff is converted into a feature point coordinate sequence, which is composed of the spatial position information of the endpoints, bifurcation points, and arc points of the fingerprint lines; the feature point coordinate sequence is digitally encoded to generate a 128-bit fingerprint feature code, which is composed of a 64-bit main feature code and a 64-bit auxiliary feature code; a main index item and an associated index item are established in the data structure of the mapping table, the main index item stores the staff's identity code, and the associated index item stores the fingerprint feature code, name, work number, department, job level and contact information respectively; a status identification bit is set in the mapping table, and the status identification bit is used to mark the staff's on-the-job status, authority status and fingerprint data validity; the mapping table adopts a hash storage structure, and the identity code is used as the hash key value to achieve fast retrieval; the fingerprint feature code and identity information are encrypted in the mapping table by the national secret SM4 group, and the encryption process adopts the following encryption formula:

[0073] C=SM4(K,P⊕T)⊕H(R||S)

[0074] Wherein, C is the ciphertext output, K is the 16-byte encryption key, P is the plaintext data, T is the timestamp confusion factor, R is the random number sequence, S is the session identifier, H is the SM3 hash function, ⊕ represents the XOR operation, and || represents the concatenation operation; the timestamp confusion factor T is generated by the following formula:

[0075] T=(t×α1+β)mod2 128

[0076] Wherein, t is the current UNIX timestamp, α1 is a prime multiplier, and β is a random offset; the mapping table sets an update timestamp to record the creation time, the last modification time, and the expiration time of the data.

[0077] It should be noted that an intelligent lock control unit is set on the substation access control lock. The intelligent lock control unit adopts a modular design structure, including a central processing module, a fingerprint collection module, a wireless communication module, a power management module and an execution control module; the central processing module adopts a dual-core 32-bit microprocessor with a main frequency of 1.2GHz and an independent neural network operation unit; the fingerprint collection module adopts an optical fingerprint sensor with a sensing area of ​​20mm×20mm and a resolution of 500DPI, and is equipped with a moisture-proof, dust-proof and anti-static protection structure; the wireless communication module supports dual-band WiFi and low-power Bluetooth communication, with working frequency bands of 2.4GHz and 5GHz respectively, and a communication distance of not less than 50 meters; the power management module includes 1 8650 lithium battery pack and solar charging unit, the capacity of the lithium battery pack is 3000mAh, and the conversion efficiency of the solar charging unit is not less than 22%; the execution control module adopts a permanent magnet electronic lock core, and the unlocking response time is less than 0.5 seconds; the modules exchange data through the RS485 bus, and the data transmission rate is 115200bps; the shell of the intelligent lock control unit is made of aluminum alloy with a waterproof grade of IP65, and the operating temperature range is -20℃ to 70℃; a temperature sensor, a humidity sensor and a voltage sensor are arranged inside the intelligent lock control unit for real-time monitoring of the working status of the equipment; the intelligent lock control unit is fixed to the access control lock by 4 M6 anti-detachment bolts, and an anti-dismantling sensor switch is provided.

[0078] S2: When the staff member performs fingerprint verification, the fingerprint collection module of the intelligent lock control unit collects the on-site fingerprint information of the staff member, and sends the on-site fingerprint information to the authority database for comparison through the wireless communication module;

[0079] When the staff performs fingerprint verification, the fingerprint collection module of the intelligent lock control unit collects the staff's on-site fingerprint information, and the on-site fingerprint information includes the grayscale data of the fingerprint image, the feature point distribution data and the fingerprint pattern direction data; the fingerprint collection module pre-processes the collected fingerprint image, wherein the image enhancement adopts the improved adaptive histogram equalization algorithm:

[0080] L(x,y)=α·H(x,y)+(1-α)·G(x,y)

[0081] Wherein, L(x, y) is the enhanced image, H(x, y) is the histogram equalization result, G(x, y) is the Gaussian filtering result, and α is the adaptive weight coefficient; the calculation formula of α is:

[0082]

[0083] Among them, σ 2is the local area variance, k is the adjustment factor, ω(x,y) is the local texture complexity; the texture complexity calculation formula is:

[0084]

[0085] in, is the image gradient, M and N are the local window sizes, and V is the normalization factor.

[0086] Extract feature point data from the preprocessed fingerprint image, including endpoint features, bifurcation point features and singular point features, generate a feature point vector set P = {p1, p2, ..., pn}, and the weight coefficient of the feature point is calculated by the following formula:

[0087] W(p i )=μ·R(p i )+λ·Q(p i )

[0088] Among them, R(p i ) is the reliability of the characteristic point, Q(p i ) is the quality score, μ and λ are weight factors; the reliability calculation formula is:

[0089]

[0090] Among them, d is the distance from the feature point to the nearest singular point, and D is the feature point density parameter.

[0091] Preferably, an adaptive weight coefficient α based on local texture characteristics is introduced in the image enhancement stage. The term nonlinearly maps the variance of the local area, and at the same time, the (1-ω(x,y)) term is combined to introduce feedback adjustment of texture complexity, thus realizing dynamic adaptation of enhanced strength. The calculation of texture complexity ω(x,y) adopts the accumulation form of normalized gradient energy, and the richness of texture is characterized by the sum of squares of gradient amplitudes in the local window. This method not only considers the severity of local changes, but also realizes scale invariance through the MNV term. In the feature point evaluation stage, the reliability R(p i ) reuses the texture complexity ω(x,y) and introduces The spatial correlation with the singular point is established by using the term, forming a closed-loop feedback mechanism from image enhancement to feature extraction. This design not only improves the adaptability and robustness of the algorithm, but also ensures the accuracy and reliability of feature extraction through the complementary correlation between parameters.

[0092] The feature point vector set is normalized to generate a 128-bit fingerprint feature descriptor; the wireless communication module uses the SM2 elliptic curve encryption algorithm to encrypt the fingerprint feature descriptor, and the encryption key length is 256 bits; the wireless communication module packages the encrypted fingerprint feature descriptor and the device unique identification code and sends them to the authority database server through the WiFi channel.

[0093] S3: Sending an unlocking command or a rejecting command to the intelligent lock control unit according to the comparison result, and recording the collection time, collection location and unlocking status of the on-site fingerprint information;

[0094] When sending an unlocking command or a rejection command to the intelligent lock control unit according to the comparison result, the similarity score S between the on-site fingerprint feature descriptor and the fingerprint feature descriptor stored in the authority database is first calculated. The calculation formula of the similarity score is:

[0095]

[0096] Among them, M is the matching degree of feature points, N is the consistency of texture direction, O is the similarity of core point area, and β1, β2, and β3 are the corresponding weight coefficients.

[0097] The calculation of the feature point matching degree is shown in the following formula:

[0098]

[0099] Among them, θ i is the direction difference angle of the i-th pair of matching feature points, W(p i ) is the feature point weight, d i is the spatial distance of feature points, and D is the distance normalization parameter.

[0100] The calculation of the texture direction consistency is as follows:

[0101]

[0102] Among them, Φ(x, y) is the direction field matrix of the current fingerprint, Ψ(x, y) is the direction field matrix of the fingerprint in the authority database; the calculation formula of the direction field is:

[0103]

[0104] Among them, G xy , G xx , G yy is the directional gradient component of the fingerprint image;

[0105] Among them, the core point area similarity is shown as follows:

[0106]

[0107] Among them, V c is the feature vector of the current fingerprint core point, V t is the fingerprint core point feature vector in the authority database, δ is the core point position deviation, Δ is the allowable threshold, is the Gaussian kernel parameter;

[0108] When the similarity score S≥85 and the verification time is within the authorized period of the staff, and the staff status is on duty and the authority is not restricted, an unlocking instruction is generated, and the unlocking instruction includes: device identification code, authorization code, unlocking time limit and operation sequence number;

[0109] When the similarity score is within the range of 75≤S<85 or the verification time exceeds the authorization period ±15 minutes, a first rejection instruction is generated, the first rejection instruction includes: a device identification code, a rejection reason code, an operation sequence number, and a warning level; the first rejection instruction is insufficient authority;

[0110] When the similarity score is less than 75, or the verification time exceeds the authorization period ±60 minutes, or the number of repeated verification failures exceeds 3 times within a short period of time (5 minutes), a second rejection instruction is generated. The second rejection instruction includes: device identification code, rejection reason code (0x02), operation sequence number and warning level (level 2).

[0111] The unlocking instruction and the rejection instruction are encrypted using the SM4 block cipher algorithm, and the key update cycle is 4 hours; the system synchronously records the verification event information, including: the staff identity code, the 64-bit hash value of the on-site fingerprint feature descriptor, the collection time (accurate to milliseconds), the GPS coordinate information of the collection location (longitude, latitude, altitude), the lock number, the similarity score, the verification result and the response time;

[0112] S4: Generate a lock status log based on the collection time, collection location and unlocking status, and display the lock status log on a display terminal of the substation monitoring center.

[0113] Furthermore, this embodiment also provides a substation personnel authority management and lock status monitoring system, including:

[0114] A data collection module is used to collect fingerprint information and identity information of substation staff, establish a corresponding relationship between the fingerprint information and the identity information, and store them in the authority database;

[0115] A comparison module, used to send the on-site fingerprint information to the authority database for comparison through the wireless communication module;

[0116] An instruction sending module, used to send an unlocking instruction or a rejecting instruction to the intelligent lock control unit according to the comparison result, and record the collection time, collection location and unlocking status of the on-site fingerprint information;

[0117] The log generation module is used to generate a lock status log based on the collection time, collection location and unlocking status, and display the lock status log on a display terminal of the substation monitoring center.

[0118] This embodiment also provides a computer device, which is suitable for the method of substation personnel authority management and lock status monitoring, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of substation personnel authority management and lock status monitoring proposed in the above embodiment.

[0119] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0120] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the authority management of substation personnel and the lock status monitoring proposed in the above embodiment is implemented.

[0121] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0122] Example 2

[0123] This is the second embodiment of the present invention. This embodiment provides a method for substation personnel authority management and lock status monitoring. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0124] The experiment was conducted at the XX substation of a provincial power company from January 15, 2024 to February 15, 2024, a total of 32 days. The experimental subjects included 30 staff members from different departments and job levels in the substation, including 3 A-level management personnel, 12 B-level maintenance personnel, and 15 C-level general staff members.

[0125] The preliminary preparations for the experiment include:

[0126] Hardware equipment deployment: 10 sets of smart lock control units, model SLC-2024X, are installed at the entrances of important areas of the substation. Each set of equipment is equipped with a 500DPI high-precision optical fingerprint sensor and a dual-band WiFi+BLE wireless communication module.

[0127] Software system deployment: Build a distributed permission management database system, use Huawei Kunpeng servers as local computing nodes, Alibaba Cloud servers as remote backup nodes, and achieve real-time data synchronization between the two nodes through a VPN dedicated line.

[0128] Personnel information collection: Use the URU5000 fingerprint collector to collect fingerprint information from the thumb and index finger of each staff member. Each fingerprint is collected 5 times for feature extraction. At the same time, enter detailed identity information and establish a mapping relationship.

[0129] Permission level settings:

[0130] Class A authority: full access to core areas such as control room, power distribution room, relay protection room, etc.

[0131] Class B authority: Limited access rights to important areas such as equipment rooms and cable mezzanines

[0132] C-level authority: basic access rights to common areas such as office areas and duty rooms

[0133] During the experiment, the following technical indicators were verified:

[0134] Fingerprint collection quality: Scored by the NIST fingerprint image quality assessment algorithm

[0135] Feature extraction accuracy: Verify the repeatability and stability of feature point extraction

[0136] Identify matching performance: Calculate FAR (false acceptance rate) and FRR (false rejection rate)

[0137] System response time: the whole process delay from fingerprint collection to verification result output

[0138] Environmental adaptability: system reliability under different temperature and humidity conditions

[0139] Security performance: effectiveness of key update mechanism and encrypted communication

[0140] To ensure the objectivity and reliability of experimental data, the following measures are taken:

[0141] Staff with different levels of authority are randomly selected for verification testing during each period

[0142] Records detailed process data for all authentication attempts

[0143] Simulate various abnormal situations for stress testing

[0144] Hire a third-party agency to conduct a safety assessment

[0145] The test data are recorded in the following table:

[0146] Table 1 Experimental data table

[0147]

[0148] Through statistical analysis of experimental data, the present invention has significant technical advantages over traditional systems:

[0149] The average fingerprint quality score of the system of the present invention reaches 91.52, which is 11.2% higher than the 82.3 of the traditional system. This is mainly due to the application of the improved adaptive histogram equalization algorithm and the new optical fingerprint sensor. In particular, the fingerprint quality scores of users with A-level authority are maintained above 93, ensuring the reliability of access control in key areas.

[0150] Experimental data show that the average feature matching score of the present invention is 87.94, which is 12% higher than that of the traditional system. This improvement mainly comes from the reliability evaluation mechanism introduced in the feature point weight calculation formula, which fully considers the distance relationship from the feature point to the singular point and the local texture complexity.

[0151] The average verification response time of the present invention is 451.67ms, which is 34.1% faster than the 685ms of the traditional system. This significant performance improvement is attributed to:

[0152] Hardware architecture using dual-core 32-bit microprocessor

[0153] Optimized hash storage structure

[0154] Improved feature extraction algorithm

[0155] Verify reliability:

[0156] The average verification success rate reached 96.99%, an increase of 9.6% over the traditional system. At the same time, the abnormal operation detection rate increased to 95.57%, an increase of 12.2% over the traditional system. This shows that the present invention has made significant progress in accuracy and security.

[0157] By introducing SM2 elliptic curve encryption and SM4 block cipher algorithms, the system security rating reaches 93.8, which is 9.7% higher than the traditional system. It performs particularly well in preventing replay attacks and identity forgery, thanks to the application of timestamp obfuscation factors and dynamic key update mechanism.

[0158] The system maintains stable operation in the temperature range of -20℃ to 70℃, and the IP65 protection level ensures reliability in humid environments. This solves the problem that traditional systems are prone to failure in extreme environments.

[0159] Through comparative analysis, it can be seen that the present invention has achieved the expected technical effects in terms of fingerprint collection quality, feature matching accuracy, system response time, verification reliability and security, and is significantly better than the existing technical solutions. In particular, the comprehensive improvement in core indicators provides a more reliable technical guarantee for substation safety management.

[0160] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for substation personnel authority management and lock status monitoring, characterized in that: include: Collect fingerprint information and identity information of substation staff, establish a corresponding relationship between the fingerprint information and the identity information and store them in the authority database; When the staff member performs fingerprint verification, the fingerprint collection module of the intelligent lock control unit collects the staff member's on-site fingerprint information, and sends the on-site fingerprint information to the authority database for comparison through the wireless communication module; Sending an unlocking command or a rejecting command to the intelligent lock control unit according to the comparison result, and recording the collection time, collection location and unlocking status of the on-site fingerprint information; The collection time, collection location and unlocking status are used to generate a lock status log, and the lock status log is displayed on a display terminal in a substation monitoring center.

2. The method for substation personnel authority management and lock status monitoring according to claim 1, characterized in that: The identity information includes the staff member’s name, work number, department, job level and contact information; The fingerprint information is stored in binary form, and a one-to-one mapping table is established with the identity information; a unique identity code is assigned to each staff member in the mapping table; According to the job level of the staff, different access control authority levels are set in the mapping table. The access control authority levels are divided into Class A, Class B and Class C, where Class A has access to all areas, Class B has access to important areas, and Class C only has access to ordinary areas.

3. The method for substation personnel authority management and lock status monitoring according to claim 2, characterized in that: Establishing a corresponding relationship between the fingerprint information and the identity information and storing them in the authority database includes: Converting the fingerprint feature data of the staff member into a feature point coordinate sequence, wherein the feature point coordinate sequence is composed of the spatial position information of the end points, bifurcation points, and arc points of the fingerprint pattern; Digitally encoding the feature point coordinate sequence to generate a 128-bit fingerprint feature code; Encrypting the fingerprint feature code and identity information in the mapping table, and storing the encrypted data in the authority database; The encryption can be expressed as follows: C=SM4(K,P⊕T)⊕H(R||S) Where C is the ciphertext output, K is the 16-byte encryption key, P is the plaintext data, T is the timestamp confusion factor, R is the random number sequence, S is the session identifier, H is the SM3 hash function, ⊕ represents the XOR operation, and || represents the concatenation operation; The calculation of the timestamp confusion factor T can be expressed by the following formula: T=(t×a1+β)mod 2 128 Where t is the current UNIX timestamp, a1 is the prime multiplier, and β is the random offset.

4. The method for substation personnel authority management and lock status monitoring as claimed in claim 3, characterized in that: The fingerprint information includes grayscale data of the fingerprint image, feature point distribution data and fingerprint pattern direction data; The fingerprint collection module pre-processes the collected fingerprint image; Extract feature point data from the preprocessed fingerprint image and generate a feature point vector set; The feature point data includes endpoint features, bifurcation point features and singular point features.

5. The method for substation personnel authority management and lock status monitoring according to claim 4, characterized in that: The preprocessing includes image enhancement, which can be expressed by the following formula: L(x,y)=α·H(x,y)+(1-α)·G(x,y) Among them, L(x,y) is the enhanced image, H(x,y) is the histogram equalization result, G(x,y) is the Gaussian filtering result, and α is the adaptive weight coefficient; The calculation of the adaptive weight coefficient α can be expressed by the following formula: Among them, σ 2 is the local area variance, k is the adjustment factor, and ω(x,y) is the local texture complexity; The calculation of the texture complexity ω(x, y) can be expressed by the following formula: in, is the image gradient, M and N are the local window sizes, and V is the normalization factor.

6. The method for substation personnel authority management and lock status monitoring according to claim 5, characterized in that: The feature point vector set also includes the weight coefficient of each feature point, which is calculated by the following formula: W(p i )=μ·R(p i )+λ·Q(p i ) Among them, R(p i ) is the reliability of the characteristic point, Q(p i ) is the quality score, μ and λ are weight factors; The calculation of the reliability of the feature point can be expressed by the following formula: Among them, d is the distance from the feature point to the nearest singular point, and D is the feature point density parameter.

7. The method for substation personnel authority management and lock status monitoring according to claim 6, characterized in that: The comparison result is judged according to the similarity score; The similarity score is calculated as follows: Among them, M is the matching degree of feature points, N is the consistency of texture direction, O is the similarity of core point area, and β1, β2, and β3 are corresponding weight coefficients; The feature point matching degree can be expressed by the following formula: Among them, θ i is the direction difference angle of the i-th pair of matching feature points, W(p i ) is the feature point weight, d i is the spatial distance of feature points, and D is the distance normalization parameter.

8. The method for substation personnel authority management and lock status monitoring according to claim 7, characterized in that: When the similarity score is greater than a first preset threshold, the verification time is within the authorized period of the staff member, and the staff member is in office and has no restricted authority, an unlocking instruction is generated, and the unlocking instruction includes: a device identification code, an authorization code, an unlocking time limit, and an operation sequence number; When the similarity score is within the preset range or the verification time exceeds the authorization period ±15 minutes, a first rejection instruction is generated; when the similarity score is less than the second preset threshold, or the verification time exceeds the authorization period ±60 minutes, or the number of repeated verification failures exceeds 3 times in a short period of time, a second rejection instruction is generated, and the second rejection instruction includes: device identification code, rejection reason code, operation sequence number and warning level.

9. A system for substation personnel authority management and lock status monitoring, based on the method for substation personnel authority management and lock status monitoring according to any one of claims 1 to 8, characterized in that: include: A data collection module is used to collect fingerprint information and identity information of substation staff, establish a corresponding relationship between the fingerprint information and the identity information, and store them in the authority database; A comparison module, used to send the on-site fingerprint information to the authority database for comparison through the wireless communication module; An instruction sending module, used to send an unlocking instruction or a rejecting instruction to the intelligent lock control unit according to the comparison result, and record the collection time, collection location and unlocking status of the on-site fingerprint information; The log generation module is used to generate a lock status log based on the collection time, collection location and unlocking status, and display the lock status log on a display terminal of the substation monitoring center.

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