Power distribution terminal whole-process management and operation system based on whole life cycle

Abstract

The application discloses a kind of based on full life cycle's power distribution terminal whole process management and control operation and maintenance system, it is related to management resource scheduling and process management technical field. Including: node identification mapping module extracts business association identification and is mapped into global identification using coding bidirectional mapping mechanism, and the logical management node is executed and the node identification coding of right generation is generated;State feature association module is based on guarantee priority weight and executes nonlinear association and coupling calculation to generate node state characteristic value;Power distribution terminal node audit module writes node state characteristic value into node state database to execute verification, activates life cycle management state machine and monitors node data flow.Satisfy the preset scrap evolution termination condition, call logical cancellation and trigger resource redundancy compensation program, execute deviation comparison analysis and resource configuration parameter correction.The application realizes the closed-loop management and control of terminal node in data flow and state evolution flow whole process by constructing full life cycle data business flow closed-loop management and control architecture.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of life cycle management, in particular to a full-process management and operation system for power distribution terminals based on a full life cycle. BACKGROUND

[0002] At present, the operation monitoring and management of distributed power distribution terminals has entered the digital stage. The existing technology usually tries to evaluate the terminal loss by using statistical models, or records the installation and inspection progress of the equipment at the field nodes through mobile terminals to obtain discrete operation data.

[0003] However, in the actual operation of the distributed information processing system, the heterogeneous identification system, multi-dimensional node attribute and system global resource configuration model are often in a logical island state. Due to the lack of an adaptive closed-loop state synchronization mechanism that can cover the full evolution path, when the Internet of Things terminal node occurs logical location offset, associated handle change or modal adjustment in the state migration process, the state database at the control end cannot real-time sense the timing dynamic change of the execution end. The full life cycle timing tracking mechanism of the node is invalid, which easily causes logical mismatch of data mirroring and physical state.

[0004] This non-closed-loop architecture seriously weakens the accuracy of dynamic resource scheduling and steady-state control of the distributed system when processing large-scale heterogeneous node data. Since the system cannot real-time verify and eliminate invalid nodes with logical failure, the control master station needs to continuously scan and handshake with redundant nodes, resulting in serious waste of computing resources and unnecessary loss of communication bandwidth. In addition, this timing state synchronization conflict and the lack of logical mapping dimension will eventually cause serious feedback lag when the system executes the global resource scheduling strategy, affecting the overall steady-state operation of the heterogeneous management and control cluster.

[0005] In summary, an automatic collaborative management and control mechanism needs to be constructed to solve the information system processing failure problem caused by the timing state synchronization conflict, the lack of logical mapping dimension and the resource scheduling feedback lag of heterogeneous nodes in the long-period data evolution process.

[0006] Therefore, a full-process management and operation system for power distribution terminals based on a full life cycle is proposed. SUMMARY

[0007] The purpose of the present application is to provide a full-process management and operation system for power distribution terminals based on a full life cycle, which realizes closed-loop management and operation of terminal nodes in the full process of data flow and state evolution flow.

[0008] To achieve the above purpose, the present application provides the following technical solutions:

[0009] The full-process management and operation system for power distribution terminals based on a full life cycle comprises:

[0010] Node identification mapping module: extract the service association identification initially loaded by the power distribution terminal, map the service association identification to a global identification using a bidirectional mapping mechanism, perform authentication on the logical management node of the power distribution terminal based on the global identification, and generate a node identification code containing the organization node identification;

[0011] State feature association module: extract static resource load features and dynamic operation risk features corresponding to the node identification code, and perform nonlinear association based on the protection priority weight of the power distribution terminal feeder; perform coupling calculation using the static resource load features and the dynamic operation risk features to generate a node state feature value;

[0012] Power terminal node audit module: write the node state feature value into the node state database to perform verification, activate the life cycle management state machine after confirming that the business flow state has no conflict; monitor the node data flow of the entire life cycle of the power terminal, call logical cancellation and trigger resource redundancy compensation procedures when the node data flow meets the preset scrap evolution termination condition; compare the initial resource configuration feature value fed back by the logical interface according to the compensation data generated by the resource redundancy compensation procedure and perform resource configuration parameter correction to establish a full life cycle data business flow closed-loop management architecture.

[0013] Preferably, the specific process of the node identification mapping module mapping the service association identification to a global identification includes: extracting the service association identification of the power distribution terminal, identifying the resource category attribute carried by the service association identification, mapping the resource category attribute to a preset logical level using a bidirectional mapping mechanism, establishing a bidirectional logical authentication between the service association identification and the logical level, and outputting the global identification.

[0014] Preferably, the specific process of the node identification mapping module generating a node identification code includes: extracting the logical management node corresponding to the power distribution terminal commissioning plan, identifying the node attribute features in the logical management node; based on the global identification, performing logical placement authentication on the node attribute features, and performing field feature fusion using the authenticated node attribute features and the global identification to generate the node identification code.

[0015] Preferably, the specific process of the state feature association module performing weighted association based on the protection priority weight includes: identifying the logical node attributes of the feeder where the power distribution terminal is located, and establishing the protection priority weight based on the preset configuration criteria; using the protection priority weight to establish a weighted association matrix of the static resource load features, the dynamic operation risk features, and the organization node identification in the node identification code, and completing the nonlinear mapping compensation by performing nonlinear mapping transformation on the weighted association matrix.

[0016] Preferably, the specific process that the state feature correlation module generates the node state feature value comprises the following steps: calling the static resource load feature and the guarantee priority weight, establishing a node feature mapping model by using a weighted linear aggregation algorithm based on resource allocation logic, taking the guarantee priority weight as a feature adjustment variable, performing dynamic proportion coupling analysis of each component in the static resource load feature, and generating a node state feature value reflecting resource guarantee intensity distribution through multi-dimensional data feature extraction.

[0017] Preferably, the specific process that the power distribution terminal node audit module activates the life cycle management state machine comprises the following steps: writing the node state feature value into a node state database, comparing historical state data stored in the node state database and performing path consistency verification, confirming that there is no conflict in the state migration path, and constructing the life cycle management state machine by using finite state machine theory; the life cycle management state machine takes initialization, installation and deployment, operation and maintenance and logical logout of the terminal node as a logical mode, constructs a mode evolution sequence, establishes state migration driving logic between the logical modes according to the mode evolution sequence, and configures the initial active node of the power distribution terminal to activate the full life cycle data tracking.

[0018] Preferably, the specific process that the power distribution terminal node audit module calls the node logical logout and triggers the resource redundancy compensation program comprises the following steps: monitoring node data stream output by the life cycle management state machine, comparing preset scrap evolution termination conditions in the node state database; when the node data stream meets the scrap evolution termination conditions, the node logical logout is called, and the node identification code corresponding logical state logout processing is performed; the resource redundancy compensation program is triggered synchronously, and a preset redundancy configuration threshold in the initial resource configuration feature value is extracted by using a logical interface.

[0019] Preferably, the specific process that the power distribution terminal node audit module performs resource configuration parameter correction comprises the following steps: calling resource gap calculation logic in the resource redundancy compensation program, matching the resource gap amount generated by system active attribute logout processing with the redundancy configuration threshold to generate a resource compensation feature vector; extracting the initial resource configuration feature value, performing deviation comparison analysis on the resource compensation feature vector and the initial resource configuration feature value, and quantifying a logical deviation value of the analysis result; performing resource configuration parameter correction according to the logical deviation value, and feeding back the corrected resource configuration parameter to an initial configuration link by using a logical interface to construct an adaptive closed-loop management and control architecture of the full life cycle data state stream.

[0020] Compared with the prior art, the power distribution terminal node audit method has the following beneficial effects:

[0021] 1.The application extracts deep features of multi-source associated identifiers through a node identifier mapping module, and realizes logical peer-to-peer conversion from service identifier to global identifier and then to node identifier code in a heterogeneous data environment by combining a coding bidirectional mapping mechanism. This mechanism solves the data index conflict and time sequence logical fault caused by the non-uniformity of the identifier system in the cross-system evolution of heterogeneous nodes, ensures the global uniqueness of the terminal node in the distributed complex organization topology architecture, and significantly improves the certainty of cross-source data fusion and the accuracy of full-path tracing between heterogeneous systems.

[0022] 2.The application synchronously extracts static resource load features, running risk features, and configuration weights using a state feature association module, performs dynamic proportion coupling analysis of multi-dimensional features through a weighted linear aggregation algorithm, and converts isolated discrete indicators into state feature values representing node control priority. This method realizes the transition of control strength from subjective logic to objective data modeling, which provides a high-precision driving benchmark with physical feedback support for state machine mode migration, effectively reduces the calculation redundancy caused by state misjudgment, and improves the real-time performance and quantitative accuracy of the system in perceiving the evolution state of the node.

[0023] 3.The application realizes adaptive feedback correction of initial resource configuration parameters using a deviation vector through a life cycle management state machine in the power distribution terminal node audit module, which combines the analysis of logout instruction triggering, resource redundancy compensation, and deviation comparison of initial configuration feature values. This closed-loop architecture solves the non-closed-loop conflict between the system planning model and the physical running state, can timely shield the signal flow of invalid nodes at the system level, significantly reduces the occupation of system communication bandwidth and calculation overhead by invalid data processing, and ensures the stable operation of the heterogeneous control cluster and the optimal scheduling of resource distribution. BRIEF DESCRIPTION OF DRAWINGS

[0024] Fig. 1 FIG. 1 is a structural diagram of a full-process control and operation and maintenance system for power distribution terminals based on the full life cycle of the application;

[0025] Fig. 2 FIG. 2 is a flowchart of a full-process control and operation and maintenance system for power distribution terminals based on the full life cycle of the application;

[0026] Fig. 3 FIG. 3 is a schematic diagram of the life cycle management state jump logic based on the finite state machine of the embodiment of the application. DETAILED DESCRIPTION

[0027] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0028] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application. Figs. 1 to 3 The present application provides a full-process management and operation and maintenance system for power distribution terminals based on a full life cycle, and the technical solutions are as follows:

[0029] Embodiment 1

[0030] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application. Fig. 1 、 Fig. 2 The present application provides a full-process management and operation and maintenance system for power distribution terminals based on a full life cycle, and the technical solutions are as follows:

[0031] The node identification mapping module extracts the business association identifier initially loaded by the power distribution terminal, maps the business association identifier to a global identifier using a bidirectional encoding mapping mechanism, performs authentication on the logical management node of the power distribution terminal based on the global identifier, and generates a node identification code containing an organizational node identifier.

[0032] The state feature association module extracts the static resource load features and dynamic operation risk features corresponding to the node identification code, and performs nonlinear association based on the priority weight of the power distribution terminal feeder protection. Coupling calculation is performed using the static resource load features and dynamic operation risk features to generate a node state feature value.

[0033] The power distribution terminal node audit module writes the node state feature value into the node state database for verification, activates the life cycle management state machine after confirming that the business flow state is conflict-free, monitors the node data flow of the power distribution terminal full life cycle, calls logical cancellation and triggers resource redundancy compensation procedures when the node data flow meets the preset scrap evolution termination condition, compares the initial resource configuration feature value fed back by the logical interface according to the compensation data generated by the resource redundancy compensation procedure, and performs resource configuration parameter correction to establish a full life cycle data business flow closed-loop management architecture.

[0034] Further, the specific process of the node identification mapping module mapping the business association identifier to a global identifier includes extracting the business association identifier of the power distribution terminal, identifying the resource category attribute carried by the business association identifier, mapping the resource category attribute to a preset logical level using a bidirectional encoding mapping mechanism, establishing bidirectional logical authentication of the business association identifier and the logical level, and outputting the global identifier.

[0035] Specifically, the node identification mapping module accesses the heterogeneous data source through an application programming interface, calls the service association identification generated in the initialization stage of the power distribution terminal, and the service association identification includes a batch index code and a transaction serial number. The resource modal attribute recorded in the service association identification is extracted through data analysis, and the resource modal attribute includes a category standard code, a specification parameter, and hardware version information.

[0036] The encoding bidirectional mapping mechanism is implemented by preconfiguring a corresponding mapping table of the service level code and the logical management level, the mapping table is stored in a memory mapping database (such as Redis) and adopts a bidirectional index structure; the bidirectional mapping mechanism is implemented by constructing a mapping matrix based on a distributed hash table, the matrix stores an associated triple of a node instantiation handle, a mapping address, and a global identification, and when an identification conflict occurs, the uniqueness and reversible verification of the mapping are ensured through open addressing and rehashing. When the attribute of the logical management level changes, the mapping mechanism reversely retrieves the associated unique service association identification according to the index and in combination with the transaction serial number as a constraint condition, and returns a state synchronization instruction to the heterogeneous data source through a logical interface, so as to ensure a bidirectional logical closed loop. According to the identified category standard code, the power distribution terminal is positioned to a secondary device node and a feeder automation terminal node in a preset management space.

[0037] The bidirectional logical authentication process specifically includes: performing a check between the service association identification and the logical management level, verifying whether the resource modal attribute in the service association identification meets the device access specification preset by the logical management level. After the consistency matching of the identification logic and the control logic is passed, the system extracts the specification parameter, the hardware version information, and the transaction serial number in the resource modal attribute, and performs string splicing in a fixed field order to construct a node feature raw data stream.

[0038] To solve the irreversible information problem caused by the one-way nature of the hash algorithm, the system adopts a technical path of “digest mapping + original association storage”: the system calls a preset digest algorithm (such as SHA-256), performs a hash operation on the node feature raw data stream, maps the generated digest result into a fixed bit width hexadecimal string, and outputs a global identification; at the same time, the system stores the global identification as a primary key, and the corresponding service association identification and transaction serial number as a value in the bidirectional mapping index table. By introducing the transaction serial number as a hash salt value, it is ensured that even in the case of completely consistent hardware specifications, the generated global identification still has strong uniqueness in the logical space.

[0039] The node identification mapping module of the application establishes the peer-to-peer mapping and bidirectional authentication between the multi-source association identification and the logical management level, realizes the accurate conversion of heterogeneous data into standard node information, and ensures the identity uniqueness of the power distribution terminal in the initial link of the control system and the logical consistency of heterogeneous data processing.

[0040] Further, the specific process of the node identity mapping module generating the node identity code comprises: extracting a logical management node corresponding to a power distribution terminal commissioning plan, identifying node attribute features in the logical management node; based on a global identity, performing logical ascription on the node attribute features, performing field feature fusion on the ascribed node attribute features and the global identity, and generating the node identity code.

[0041] Specifically, the node identity mapping module retrieves the commissioning plan corresponding to the power distribution terminal from the background database, extracts the logical management node therefrom, and the logical management node represents the physical deployment space domain and the logical membership set of the power distribution terminal in the system topology architecture. The node attribute features in the logical management node are identified, and the node attribute features include maintenance unit code, grid topology number, and logical access permission level data. In the logical ascription stage, the system verifies the consistency of the grid topology number recorded in the node attribute features and the physical space position of the terminal based on the global identity by retrieving the preconfigured grid permission mapping table, and establishes the logical jurisdiction permission of the logical management node on the terminal data stream.

[0042] When performing field feature fusion, the system logically splices the node attribute feature string representing organizational affiliation and the global identity string representing resource attributes, and adds a preset level check field. The level check field is composed of the depth index value of the device in the management hierarchy tree and the logical check bit, which is used to mark the logical depth of the terminal in the "region-node-branch" multi-level management architecture, and serves as a check constraint for locking the unique business association identifier during reverse tracing. Finally, the fused composite data stream is bit integrated according to the preset field offset rule to generate the node identity code, which serves as the unique logical attribute and resource attribute dual-index certificate of the power distribution terminal in the management system.

[0043] The present application realizes the logical alignment of resource attributes and topology architecture by deeply fusing the node attribute features of the logical management node and the global identity, eliminates the logical boundary conflicts in the ascription link, and ensures that the power distribution terminal has a definite logical affiliation and traceable ownership basis in the whole life cycle management process.

[0044] Further, the specific process of the state feature association module performing weighted association based on the protection priority weight comprises: identifying the logical node attribute of the feeder where the power distribution terminal is located, and establishing the protection priority weight based on the preset configuration criteria; using the protection priority weight to establish a weighted association matrix of static resource load features, dynamic operation risk features, and organizational node identities in the node identity code, and completing nonlinear mapping compensation by performing nonlinear mapping transformation on the weighted association matrix.

[0045] Specifically, the state feature association module accesses the node state database through the internal communication interface to retrieve the recorded static resource load features of the power distribution terminal, which include the original load scalar reflecting the initial resource scale and loss state of the device, and the maintenance resource budget parameter allocated to the terminal. Through the index node identification code, the corresponding organizational node identification is retrieved from the organizational structure database, which clearly identifies the corresponding topology operation and maintenance unit of the terminal.

[0046] According to the physical location of the power distribution terminal, the logical node attributes of the feeder line where the power distribution terminal is located are identified, and the attributes are defined as the load importance level and the operation reliability threshold of the feeder line in the network topology. According to the load importance level, a preset configuration criterion is matched to determine the protection priority weight, wherein the configuration criterion is a multi-dimensional weight judgment logic table preset in the system, and by extracting the user type (such as high sensitivity, important industry or basic protection) mounted on the feeder line and the power-off sensitivity of the ring network node, the qualitative logical importance is converted into a quantitative priority adjustment coefficient.

[0047] As a preferred embodiment, the determination of the protection priority weight specifically includes: collecting the operation parameter stream of the feeder line where the power distribution terminal is located in real time, performing histogram statistics on the numerical distribution of the sequence in the observation period, dividing it into N discrete intervals, calculating the probability distribution of the numerical value of each interval, quantifying the fluctuation distribution characteristics of the operation parameter stream in the preset observation period using the information entropy algorithm, and generating the feeder line operation risk entropy; according to the feeder load importance level, an initial weight reference value is determined, and the initial weight reference value is compensated by nonlinear mapping using the feeder line operation risk entropy, to output the dynamically adjusted protection priority weight.

[0048] For example, when a certain feeder line is in a high load rate for a long time and the voltage fluctuation variance increases, the calculated operation risk entropy H significantly increases. The system takes the initial weight reference value W0 as the basis, combines the operation risk entropy H as the independent variable, and inputs the preset exponential compensation function where α is the adjustment operator, which is not a fixed constant but a sensitivity coefficient dynamically determined according to the real-time operation risk entropy of the feeder line, H is the independent variable, and e is a mathematical constant, approximately equal to 2.718. Specifically, the adjustment operator is positively correlated with the operation risk entropy, which is determined by a preset piecewise linear function: when the operation risk entropy is below the safety threshold, the adjustment operator takes the basic sensitivity value; when the operation risk entropy exceeds the safety threshold, the adjustment operator is linearly compensated with the increase of the operation risk entropy, so as to enhance the response gain of the weight to the risk fluctuation and realize the nonlinear lifting of the weight. This mechanism realizes the leap from static level evaluation to dynamic risk perception, quantifies the maintenance urgency using real-time operation parameter deviation, and improves the matching accuracy of resource allocation and actual operation risk.

[0049] The guarantee priority weight is used as a logical correlation factor, a weighted correlation matrix is constructed, and a multi-dimensional logical mapping between the static resource load characteristics and the organization node identifier is established under unified data dimensions. In the mapping process, the system first performs normalization processing on the static resource load characteristics. Specifically: the system extracts the load peak value and the resource maximum value of the historical similar assets as the reference boundary, and projects the data of different magnitudes to the dimensionless interval between 0 and 1 through a linear conversion algorithm, thereby eliminating the deviation of different indicators (such as the initial amount and the maintenance amount) in the numerical magnitude. Subsequently, the system performs matrix multiplication operation on the normalized values and the adjusted guarantee priority weight, maps the operation result to the resource allocation index space corresponding to the organization node identifier, and completes the weighted correlation processing.

[0050] The system extracts the running risk entropy of the logical node (quantified based on the measured current fluctuation) and combines the static resource load characteristic vector to construct a multi-dimensional mapping matrix. The matrix uses a nonlinear coupling algorithm to map the physical parameters to node state characteristic values. The characteristic values directly participate in the data communication frequency scheduling of the terminal node and the automatic tuning of the abnormal detection algorithm sensitivity.

[0051] The present application realizes the deep correlation of the distribution terminal resource value, the operation and maintenance quota, and the organizational attribution. By introducing the feeder running risk entropy, the transition from static evaluation to dynamic perception is realized, the maintenance urgency is quantified by using real-time running parameter deviation, and the matching accuracy of resource allocation and power grid actual running risk is significantly improved.

[0052] Further, the specific process of the state characteristic correlation module generating the node state characteristic value includes: calling the static resource load characteristics and the guarantee priority weight, establishing a node characteristic mapping model based on resource allocation logic, using a weighted linear aggregation algorithm, taking the guarantee priority weight as a characteristic adjustment variable, performing dynamic proportion coupling analysis of each component in the static resource load characteristics, and generating a node state characteristic value reflecting the resource guarantee intensity distribution through multi-dimensional data characteristic extraction.

[0053] Specifically, the state characteristic correlation module calls the quantized static resource load characteristics (including the original load scalar and the maintenance resource budget parameter) from the system cache and the guarantee priority weight adjusted by the feeder running risk entropy. Based on the resource allocation logic, a node characteristic mapping model is established. The resource allocation logic refers to a pre-constructed mapping matrix of resource size and control state, which is used to define the management mode required to match different resource intervals under a certain running risk, and to convert discrete heterogeneous indicators into management evaluation benchmarks in a unified context.

[0054] As a preferred embodiment, the node feature mapping model established by using the weighted linear aggregation algorithm specifically comprises: performing normalization mapping on the original load scalar and the maintenance resource budget parameter, and converting dimensional data into interval equivalent resource feature values. An index mapping function based on the support priority is constructed by using the support priority weight as a feature adjustment variable.

[0055] Specifically, the index mapping function based on the support priority is constructed by calculating , wherein is the normalized maintenance resource budget parameter, is the support priority weight, is the index mapping function, and the nonlinear adjustment of the maintenance resource in the feature space distribution density is realized. Then, the system performs dynamic proportion coupling analysis of each component in the static resource load feature, and evaluates the resource input sensitivity corresponding to the unit terminal node data throughput size. In the multi-dimensional data feature extraction stage, the system inputs the normalized load feature, the scaled budget feature, and the organization node identifier into a multi-dimensional feature fusion operator.

[0056] The multi-dimensional feature fusion operator realizes dimension collapse processing through principal component projection. The specific process is as follows: the normalized load feature, the scaled budget feature, and the numerical vector generated after performing one-hot encoding or feature hash mapping on the organization node identifier are collectively constructed into a multi-dimensional feature vector (wherein L is the load feature, B' is the scaled budget feature, and O is the organization node identifier); and a feature mapping matrix is used, and the construction process of the feature mapping matrix is as follows: the feature data of historical nodes are collected to construct a sample set, the covariance matrix of the sample set is calculated, and the covariance matrix is subjected to eigenvalue decomposition, the first K eigenvectors with the largest eigenvalues are selected to form a projection matrix P (i.e., principal component analysis method), and linear projection transformation is performed on the multi-dimensional feature vector , wherein is the node state feature value, is the multi-dimensional feature vector, is the feature mapping matrix, is the feature mapping matrix. Transposition maps the three-dimensional feature to a one-dimensional feature space, and the principal component value after projection is selected as the node state feature value reflecting the resource support intensity distribution.

[0057] The node state feature value is stored in the form of a feature vector. Specifically, the coupling calculation of the node state feature value is a process of collapsing a multi-dimensional enhanced feature into a one-dimensional scalar value by using a feature mapping matrix to perform linear projection transformation, by taking the guarantee priority weight as a multiplication operator acting on the feature vector composed of the normalized load feature and the budget feature. The node state feature value is a digital fingerprint for identifying the resource distribution state of the audit module, and has global uniqueness and state sensitivity. The present scheme eliminates the dimensional gap between physical scale and operation and maintenance resources by introducing a nonlinear scaling and dimension collapse mechanism, enhances the recognition of high guarantee level nodes in the management feature space, and ensures the accuracy of state quantization extraction.

[0058] The present application realizes the dynamic coupling of node scale and operation and maintenance resources in the equivalent feature space through the node feature mapping model, highlights the management weight of the high guarantee level node by using a nonlinear mapping operator, eliminates the analysis bias caused by the dimensional difference, and provides a digital basis with high discrimination for the accurate state evolution of the power distribution terminal.

[0059] Further, the specific process of activating the life cycle management state machine by the power distribution terminal node audit module includes: writing the node state feature value into the node state database, comparing the historical state data stored in the node state database and performing path consistency verification, confirming that the state migration path has no conflict, and constructing the life cycle management state machine by using the finite state machine theory; the life cycle management state machine takes the initialization, installation and deployment, operation and maintenance, and logical cancellation of the terminal node as a logical mode, constructs a mode evolution sequence, establishes the state migration driving logic between the logical modes according to the mode evolution sequence, and configures the initial active node of the power distribution terminal, and activates the full life cycle data tracking. Fig. 3 The life cycle management state jump logic diagram based on the finite state machine of the embodiment of the present application.

[0060] Specifically, the power distribution terminal node audit module stores the generated node state feature value in the corresponding entry of the node state database. The node state database, as the data storage core of the system, is pre-configured with a metadata storage area and a state history area, for real-time maintenance of the state information of the power distribution terminal from initialization to logical cancellation, and for each node identification code to allocate a corresponding state evolution log index. The system extracts the historical state data stored in the node state database, performs logical operation on the mode weight associated with the current node state feature value and the historical state data, calculates the difference between the current weight and the maximum historical weight, and judges whether the current mode belongs to the legal logical follow-up.

[0061] The specific process of the execution path consistency verification includes: calling a preset topological path model, the topological path model being a node adjacency matrix constructed based on a legal state transition sequence; when performing the path consistency verification, performing time sequence feature extraction on a to-be-verified mode associated with a current node state feature value and historical state data to generate a to-be-verified state sequence.

[0062] Specifically, the node adjacency matrix A called by the power distribution terminal node audit module has rows i and columns j representing each logical mode in the life cycle, and if the matrix element represents that the mode i to the mode j is a legal jump path, then it represents that the jump is prohibited. When performing the path consistency verification, the system extracts historical management data stored in the node state database and sorts the historical management data and a current to-be-processed business link according to time stamps to generate a to-be-verified path sequence. The system performs traversal verification using a directed graph evolution rule: the specific rule is that, for each adjacent node pair in the path sequence , whether the corresponding element in the adjacency matrix is 1; if it is 0, it is determined that the current state flow violates the topological order of the directed graph, and a state conflict alarm is triggered. By mapping the business logic to the topological order verification of the adjacency matrix, a technical leap from artificial compliance check to mathematical logic constraint is realized.

[0063] After the path verification passes, a life cycle management state machine is constructed using the finite state machine theory, and the specific process is as follows: first, define a state space , which respectively corresponds to initialization, installation and deployment, operation and maintenance, and logical logout. Second, build a business evolution sequence: based on the legal jump path in the adjacency matrix, extract a node subset matching the current power distribution terminal evolution target to build a directed evolution sequence. Establish a transition driving logic: traverse the sequence, and for each group of present state nodes and next state nodes, associate and match the corresponding conversion trigger event from a preset event library. Activate asset tracking: map the current node state feature value of the power distribution terminal to the corresponding state node. The system divides the numerical interval of the feature value F into multiple logical subspaces , each of which corresponds to a node in the state space S. For example: when , it is mapped to the "operation and maintenance" mode; when , it is mapped to the "installation and deployment" mode. By capturing the conversion trigger event to drive the power distribution terminal to perform state jump in the evolution sequence, the full life cycle data tracking is activated.

[0064] The application quantizes the evolution path into an adjacency matrix and performs path audit using the directed graph topology verification logic, eliminating the conflict risk caused by fuzzy logic; by defining the construction process of the state space and the trigger, accurate mathematical modeling of the node dynamic flow process is realized.

[0065] Further, the specific process of calling node logical logout and triggering resource redundancy compensation procedure of the power distribution terminal node audit module includes: monitoring the node data stream output by the life cycle management state machine, comparing the preset scrap evolution termination condition in the node state database; when the node data stream meets the scrap evolution termination condition, calling the node logical logout, executing the node identification code corresponding logical state logout processing; synchronously triggering the resource redundancy compensation procedure, and extracting the preset redundancy configuration threshold in the initial resource configuration characteristic value by using the logical interface.

[0066] Specifically, the power distribution terminal node audit module receives the node state evolution stream fed back by the life cycle management state machine in real time, which carries the current state code and the flow event identification of the power distribution terminal. Specifically, the life cycle management state machine monitors that the node is in a running state, synchronously extracts the instantaneous fluctuation of the running risk entropy by sampling period; if the fluctuation exceeds the preset safety deviation threshold, the system immediately triggers the online fine-tuning logic, and uses the current deviation vector to perform step compensation on the weight parameters in the initial configuration model. Subsequently, when the node evolves to the termination state, the system calls the resource redundancy compensation procedure to perform global correction of the cumulative deviation. Ensure real-time adaptive alignment of control parameters and physical operation performance in the whole life cycle of the node. The audit module compares the node state evolution stream with the state termination condition stored in the node state database. The state termination condition is a logical decision set composed of multiple preset evolution termination thresholds. The system evaluates the comparison result by Boolean operation logic to obtain a qualitative termination conclusion. The preset scrap evolution termination condition includes but is not limited to logical termination caused by reaching the service life of the node, node inactivation caused by physical failure, or logical logout instruction of the management level.

[0067] The specific process of logical comparison includes:

[0068] First, the real-time running feature parameters of the power distribution terminal are parsed from the received node state evolution stream, including the current cumulative running time value and the current cumulative fault frequency value; at the same time, the preset evolution termination threshold corresponding to the terminal is retrieved from the database, i.e. the rated running life value and the upper limit value of fault frequency.

[0069] Secondly, the size comparison of numerical values is performed: the current cumulative running time value is compared with the rated running life value; at the same time, the current cumulative fault frequency value is compared with the upper limit value of fault frequency.

[0070] Finally, the system processes the comparison results by using the logical "or" gate decision criterion: if any of the above values reaches or exceeds the corresponding threshold value, it is determined that the current node state evolution stream meets the state termination condition.

[0071] When it is determined that the condition is met, the system calls a node logical logout instruction. The logical logout instruction refers to identifying and encoding the execution authority of the terminated evolved node, sending a logout instruction to the database, synchronously modifying the state bit under the identification to "logout", and blocking the subsequent jump authority of the code in the state machine, completing the logical level resource release.

[0072] While performing the logout process, the system synchronously starts a resource redundancy compensation program, uses the organization node identification contained in the node identification code as a search keyword, accesses the initial configuration database through a logical interface, and extracts the redundancy configuration threshold corresponding to the logical grid from the database. The redundancy configuration threshold is defined as the minimum online terminal node base required to maintain the stability of power distribution business operation in a specific operation grid.

[0073] The present application realizes the real-time linkage of device physical exit and system logical logout by automatically matching the node evolution state with the termination condition, eliminates the resource configuration deviation caused by data flow lag, and ensures that the resource redundancy compensation program can perform parameter extraction based on accurate real-time gap data.

[0074] Further, the specific process of the power distribution terminal node audit module performing resource configuration parameter correction includes: calling the resource gap calculation logic in the resource redundancy compensation program, matching the node gap amount produced by the system active attribute logout process with the redundancy configuration threshold to generate a resource compensation feature vector; extracting the initial resource configuration feature value, performing deviation comparison analysis on the resource compensation feature vector and the initial resource configuration feature value, and quantifying the logical deviation value of the analysis result; performing resource configuration parameter correction on the logical deviation value, and feeding back the corrected resource configuration parameter to the initial configuration link through a logical interface to build an adaptive closed-loop management and control architecture of the whole life cycle data state flow.

[0075] Specifically, the power distribution terminal node auditing module calls the preset resource gap calculation logic in the resource redundancy compensation program to accurately quantify the resource compensation scale required by the current logical grid. The system calculates the resource gap by obtaining the current available resource inventory in a specific operation and maintenance grid in real time, subtracting the current available resource inventory using the preset redundancy configuration threshold as a reference, and adding the node gap produced by the active attribute logout processing of the system this time, to output the final compensation quantization scalar, and then generate a resource compensation feature vector including the compensation dimension, time priority, and logical allocation weight. The power distribution terminal node auditing module uses a logical interface to call the initial resource configuration feature value. The specific process of performing deviation comparison analysis is to extract the compensation scale value recorded in the resource compensation feature vector, and extract the preset resource allocation index in the initial resource configuration feature value. Through numerical subtraction operation, the logical deviation value reflecting the difference between the current performance evolution and the planning model is output.

[0076] If the logical deviation value is positive (i.e., the real-time compensation demand exceeds the initial configuration limit), it is determined that the current configuration model has insufficient redundancy, and the system automatically adjusts the safety redundancy weight parameter in the initial configuration model to converge the deviation.

[0077] If the logical deviation value is negative (i.e., the real-time compensation demand is lower than the initial configuration limit), it is determined that the current configuration model has excessive allocation, and the system automatically adjusts the safety redundancy weight parameter in the initial configuration model.

[0078] The specific correction process adopts a proportional feedback mechanism: according to the magnitude of the logical deviation value, the weight distribution in the initial resource configuration feature value is automatically adjusted according to the preset mapping ratio. After the correction is completed, the system transmits and feeds back the corrected resource configuration parameters to the initial configuration module in real time through a logical interface, realizing dynamic updating of the historical configuration parameters. The complete cycle of the above logical flow builds an adaptive closed-loop management and control architecture for terminal full-life-cycle data state flow in the system.

[0079] The present application realizes dynamic parameter correction from the execution layer state to the configuration layer source by establishing a deviation feedback logic based on numerical comparison, eliminates the configuration lag risk caused by running performance fluctuations by using a negative feedback adjustment mechanism, and improves the data adaptive ability and configuration accuracy of the full-life-cycle management and control system.

[0080] Embodiment 2

[0081] This embodiment provides a specific application scenario of a full-life-cycle-based power distribution terminal data closed-loop management and control system, taking the feeder automation terminal management and control process of the power supply area of an industrial park as an example. The system includes a node identification mapping module, a state feature association module, and a power distribution terminal node auditing module.

[0082] The node identification mapping module retrieves the initial loading credentials of nodes from heterogeneous data sources via a logical interface, extracting the transaction sequence number and resource modal attributes of the power distribution terminal. The system concatenates the transaction sequence number with the category standard code field and performs a hash operation to generate a unique global identifier. Then, through a bidirectional coding mapping mechanism, the system assigns it to the logical management node responsible for the maintenance of the industrial park. The system performs logical bit concatenation between the node attribute features representing logical ownership and the global identifier to generate a unique node identifier code, achieving logical alignment between node resource attributes and topology affiliation.

[0083] In the actual control process, the status feature association module first retrieves the static resource load characteristics of the target distribution terminal from the database, including its corresponding original load scalar and maintenance resource budget parameters. Since the terminal is mounted on the core production feeder of the industrial park, the system collects the real-time operating parameter stream of the line and uses the information entropy analysis method to quantify the distribution pattern of fluctuations, determines the operating risk entropy at this location, and then generates the guarantee priority weight after nonlinear mapping transformation through exponential compensation function matching.

[0084] Subsequently, the system performs normalization mapping on the two types of data—original load scalar and budget parameters—using the node feature mapping model to eliminate dimensional differences. Based on this, the system uses the aforementioned priority weights as adjustment variables, performs nonlinear scaling on the mapped data, and extracts node state feature values ​​characterizing the resource guarantee strength of the node through multi-dimensional feature fusion.

[0085] During status monitoring, the power distribution terminal node audit module stores the node status feature values ​​into the node status database. The system compares the current "operation and maintenance" mode of the terminal with the historical evolution records stored in the database by calling the topology path model defined by the node adjacency matrix A, and verifies its connectivity in the adjacency matrix to confirm that it meets the preset topological ordering constraints.

[0086] After confirming that there are no path conflicts in the evolution flow, the system activates the lifecycle management state machine and locks the terminal into the corresponding logical mode according to the directed evolution sequence. At this time, the system continuously performs real-time monitoring on the associated interfaces, and drives the state machine to switch between different logical modes by capturing state transition trigger events.

[0087] When the audit module detects that the terminal's cumulative running time has reached a preset threshold (e.g., 96 months), it performs a numerical comparison. If the system determines that the conditions for obsolescence and termination are met, it issues a node logical deregistration command, modifies the corresponding node identifier code status bit in the database to "deregister," and blocks subsequent status transition permissions for that code.

[0088] Subsequently, the system initiates a resource redundancy compensation procedure, subtracts the real-time available resource inventory from the preset redundancy configuration threshold (e.g. 100 units), and adds the node gap amount generated by the current logout process to perform resource gap quantification calculation. The system compares the generated resource compensation feature vector with the initial resource configuration feature value for deviation analysis. If the logical deviation value is positive, the system automatically increases the safety redundancy weight in the initial configuration model, and feeds back the corrected parameters to the initial configuration link through the logical interface, thereby building an adaptive closed-loop management and control architecture for the terminal full-life-cycle data state flow within the system.

[0089] Through the above adaptive closed-loop correction logic, the system realizes dynamic compensation of node configuration parameters, and improves the accuracy of state perception of heterogeneous management and control nodes. By using the linkage mechanism of logical logout instructions and resource redundancy compensation, the resource occupation and computational load of the system in handling failed nodes are effectively reduced.

[0090] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A full-lifecycle management and maintenance system for power distribution terminals, characterized in that: include: Node Identifier Mapping Module: Extracts the service association identifier initially loaded into the power distribution terminal, maps the service association identifier to a global identifier using a bidirectional coding mapping mechanism, performs authorization on the logical management node of the power distribution terminal based on the global identifier, and generates a node identifier code containing the organization node identifier; Status feature association module: Extracts the static resource load characteristics and dynamic operation risk characteristics corresponding to the node identifier code, and performs nonlinear association based on the guarantee priority weight of the distribution terminal feeder; Coupled calculations are performed using static resource load characteristics and dynamic operational risk characteristics to generate node state characteristic values; The power distribution terminal node audit module writes node status characteristic values ​​into the node status database for verification. After confirming that there are no conflicts in the business flow status, it activates the lifecycle management state machine. It monitors the node data flow throughout the entire lifecycle of the power distribution terminal. When the node data flow meets the preset scrapping evolution termination conditions, it calls the logical deregistration and triggers the resource redundancy compensation program. Based on the compensation data generated by the resource redundancy compensation program, it compares the initial resource configuration characteristic values ​​fed back by the logical interface and performs resource configuration parameter correction to establish a closed-loop management and control architecture for the entire lifecycle data business flow. The specific process of the distribution terminal node audit module calling node logic deregistration and triggering resource redundancy compensation includes: monitoring the node data stream output by the lifecycle management state machine and comparing it with the preset scrap evolution termination conditions in the node state database; when the node data stream meets the scrap evolution termination conditions, calling node logic deregistration and executing the logical state deregistration processing corresponding to the node identifier code; synchronously triggering the resource redundancy compensation program and using the logic interface to extract the preset redundancy configuration threshold from the initial resource configuration feature values; the specific process of the distribution terminal node audit module performing resource configuration parameter correction includes: calling the resource gap calculation logic in the resource redundancy compensation program, using the node gap amount produced by the system active attribute deregistration processing to match the redundancy configuration threshold, and generating a resource compensation feature vector; extracting the initial resource configuration feature values, performing deviation comparison analysis between the resource compensation feature vector and the initial resource configuration feature values, and quantifying the logical deviation value of the analysis result; performing resource configuration parameter correction for the logical deviation value, and using the logic interface to feed back the corrected resource configuration parameters to the initial configuration stage, thus constructing an adaptive closed-loop management architecture for the entire lifecycle data state stream.

2. The power distribution terminal full-process management and maintenance system based on the entire life cycle as described in claim 1, characterized in that, The specific process by which the node identifier mapping module maps the service association identifier to the global identifier includes: extracting the service association identifier of the power distribution terminal, identifying the resource category attribute carried by the service association identifier, mapping the resource category attribute to a preset logical level using a two-way coding mapping mechanism, establishing a two-way logical authentication between the service association identifier and the logical level, and generating a global identifier.

3. The power distribution terminal full-process management and maintenance system based on the entire life cycle as described in claim 1, characterized in that, The specific process of the node identifier mapping module generating node identifier codes includes: extracting the logical management node of the corresponding power distribution terminal commissioning plan, identifying the node attribute features in the logical management node; performing logical allocation and confirmation of rights for the node attribute features based on the global identifier, and using the confirmed node attribute features to perform field feature fusion with the global identifier to generate node identifier codes.

4. The power distribution terminal full-process management and maintenance system based on the entire life cycle as described in claim 1, characterized in that, The specific process of the state feature association module performing weighted association based on the priority weight includes: identifying the logical node attributes of the feeder where the distribution terminal is located, and establishing the priority weight based on the preset configuration criteria; using the priority weight to establish a weighted association matrix of static resource load characteristics, dynamic operation risk characteristics and organizational node identifier in the node identifier code; and performing nonlinear mapping transformation on the weighted association matrix to complete nonlinear mapping compensation.

5. The power distribution terminal full-process management and maintenance system based on the entire life cycle as described in claim 1, characterized in that, The specific process of generating node state feature values ​​by the state feature association module includes: retrieving the static resource load features and guarantee priority weights; establishing a node feature mapping model based on resource allocation logic using a weighted linear aggregation algorithm; using the guarantee priority weights as feature adjustment variables; performing dynamic proportion coupling analysis of each component in the static resource load features; and generating node state feature values ​​that reflect the distribution of resource guarantee intensity through multi-dimensional data feature extraction.

6. The power distribution terminal full-process management and maintenance system based on the entire life cycle as described in claim 1, characterized in that, The specific process of activating the lifecycle management state machine in the power distribution terminal node audit module includes: writing node state characteristic values ​​into the node state database, comparing the historical state data stored in the node state database and performing path consistency verification, and after confirming that there are no conflicts in the state transition path, constructing the lifecycle management state machine using finite state machine theory; the lifecycle management state machine uses the initialization, installation and deployment, operation and maintenance and logical deregistration of the terminal node as logical modes, constructs a mode evolution sequence, establishes the state transition driving logic between logical modes according to the mode evolution sequence, configures the current initial active node of the power distribution terminal, and activates full lifecycle data tracking.

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