Whole-process planning and resource coordination and management system for distribution network field operation

Abstract

The application discloses a whole-process planning and resource collaborative management system for distribution network field operation, relates to the technical field of process planning, and obtains original data of the distribution network field operation in a distribution network coverage area, and obtains a propagation chain after operation coupling propagation analysis; candidate distribution objects are identified according to the propagation chain, and a bearing difference between distribution network field teams is analyzed to obtain an operation reassignment result; path structure sensitivity is identified to lock a to-be-distributed area, a path rearrangement result with structure separability is formed, and the supportability of the distribution network field operation resource is analyzed to obtain an unbalanced area; the influence and extension constraint between the resource and the operation in the unbalanced area are analyzed to identify and eliminate a filmization area, so that the local resource aggregation and structure linkage instability problems induced by path continuous compression are inhibited, and finally, the structure transformation from local resource continuous coverage to cross-area discrete and reversible structure is realized.

Description

Technical Field

[0001] This invention relates to the field of process planning technology, specifically to a full-process planning and resource collaborative management system for distribution network field operations. Background Technology

[0002] In existing power distribution network operations, modeling is usually based on balancing the number of operations, minimizing the path, and maximizing resource utilization. It mainly relies on direct statistical analysis of operation duration, resource consumption, and spatial distance. Although it can achieve rapid allocation and initial balancing, it lacks the ability to model the structural attributes of operations and the relationships of implicit disturbances.

[0003] During task allocation, the system tends to distribute tasks evenly across multiple work groups, but fails to differentiate the degree of dependence of tasks on the external environment. This leads to the clustering of multiple tasks highly sensitive to permit windows, weather conditions, or collaborative resources within the same work group or time period, creating a structure of vulnerable node clusters. When external disturbances occur, related task chains may experience synchronous instability. In path planning, the system reduces movement costs by continuously compressing task paths, but spatially clustering multiple task nodes in a local area can result in continuous resource coverage in that area, while neighboring areas experience insufficient resource support. This causes the resource structure to shift from a discrete flow dynamic to a continuous membrane state. Furthermore, existing systems often rely on single-point tasks or local paths for risk identification, failing to identify the coupling relationship between insufficient local resource support and path structural vulnerability from the perspective of task propagation chains and resource structure. This leads to misjudging local continuous clustering structures as an overall efficient allocation state, making it difficult to identify and address potential structural imbalances. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a full-process planning and resource collaborative management system for distribution network field operations, solving the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A full-process planning and resource collaborative management system for distribution network field operations, including a propagation chain generation module, an allocation module, a risk module, and a resource mitigation module;

[0007] The propagation chain generation module is used to acquire the raw data of field operations in the distribution network coverage area, and obtain the propagation chain after operation coupling propagation analysis;

[0008] The allocation module is used to identify candidate allocation objects according to the propagation chain, analyze the load differences between distribution network field teams, and obtain the work redistribution results;

[0009] The risk module is used to identify path structure sensitivity to lock in the areas to be allocated, form path rearrangement results with structural separability, and analyze the support of distribution network field operation resources to obtain imbalance areas.

[0010] The resource resolution module is used to analyze the impact and extended constraints between resources and operations through imbalance zone analysis, in order to identify and resolve membrane zones and complete the on-site operation planning of the distribution network.

[0011] Preferably, the propagation chain generation module includes:

[0012] The data acquisition unit is used to acquire the distribution network coverage area and divide it into several grids to obtain the raw data of the distribution network field operation, including the type of power distribution equipment, coordinates of the operation point, feeder affiliation, planned time window, operation duration, type of participating work team and type of required resources.

[0013] The job disturbance unit is used to parse the environment dependency of each job execution based on the original data and obtain the disturbance vector after parameterized mapping.

[0014] The chain parsing unit is used to analyze the operation coupling propagation characteristics based on the perturbation vector and obtain the propagation chain.

[0015] Preferably, based on the perturbation vector, the operation coupling propagation characteristics are analyzed to obtain the propagation chain, including:

[0016] The similarity between nodes is calculated by vector inner product, and the coupling strength matrix is ​​obtained, whose inner elements are the coupling strength between tasks.

[0017] The information entropy of each feature within the perturbation vector is calculated based on its degree of dispersion in the global operation, and the contribution coefficient of each feature is derived using the entropy weight method to obtain the structural coupling propagation strength.

[0018] Based on the structural coupling propagation strength, an initial set is obtained, which includes multiple sets of initial nodes. For each initial node, an initial propagation path is established according to the numerical value of the corresponding row in the coupling strength matrix and the spatiotemporal constraints in the original data.

[0019] The initial propagation path is deduplicated and merged to obtain the propagation chain and its propagation gradient.

[0020] Preferably, the allocation module includes:

[0021] The load-bearing unit is used to take the structural coupling propagation strength as the demand strength, and to map the demand strength to the grid and combine it with the team's resource capacity to model the supply-demand ratio, so as to obtain the team's load-bearing capacity field in the spatiotemporal dimension.

[0022] The candidate allocation unit is used to obtain a set of sub-chain segments by performing gradient-driven operation path segmentation on the propagation chain and its propagation gradient, and to perform decentralized allocation of shift scheduling under the constraint of carrying capacity field to obtain candidate allocation objects.

[0023] The allocation result unit is used to limit the candidate allocation objects according to the same chain dispersion constraint principle, and perform consistency verification on the limited candidate allocation objects to obtain the allocation results of each sub-chain segment;

[0024] The redistribution unit is used to perform cross-shift adjustments based on the redistribution results by identifying differences in the workload of each shift and combining the work relationships, thereby generating work redistribution results.

[0025] Preferably, the risk module includes:

[0026] Planning units are used to generate job planning sequences based on job redistribution results and raw data;

[0027] The disturbance analysis unit is used to perform disturbance response propagation analysis on the planned path based on the job planning sequence, combined with the coupling relationship and propagation strength between jobs, to identify the disturbance response results, locate potential unstable points, and lock the area to be allocated, which includes at least one fracture trigger node.

[0028] The adjustment unit is used to perform break interval insertion adjustment on the area to be allocated, resulting in path rearrangement.

[0029] Preferably, the risk module also includes:

[0030] The resource data unit is used to obtain resource distribution results by correlating the resource usage of each job with the execution order of the path based on the path reordering results;

[0031] The flow analysis unit is used to determine the ability of resources within the grid to continue supporting subsequent operations based on resource distribution results, obtain resource allocation results, and identify areas with limited allocation.

[0032] The risk analysis unit is used to analyze the mismatch between the distribution of tasks and the resource acceptance relationship within the allocation-restricted area based on the allocation-restricted area and the path rearrangement results, to obtain the local resource imbalance state, and to form a resource constraint structure.

[0033] The extraction unit is used to aggregate spatially adjacent locations where local resource imbalance occurs to obtain imbalance zones, and then extract the available resources and available operations within the imbalance zones in combination with the resource constraint structure.

[0034] Preferably, the resource decomposition module includes:

[0035] The influence unit is used to analyze the propagation process of the influence of different available resources on different jobs in each path rearrangement result based on available resources, available jobs and path rearrangement results, and to obtain the structural influence degree corresponding to each available resource.

[0036] The extended constraint unit is used to extract the adjustable resources with the greatest structural influence as bottleneck resources. Based on the bottleneck resources and structural influence, the constraint degree of the bottleneck resources on the propagation link extension capability is analyzed to obtain the locking index. If the locking index exceeds the preset locking threshold, it is determined to be a membrane region.

[0037] Preferably, the resource decomposition module further includes:

[0038] The process control unit is used to rearrange the order along the path within the membrane zone, extract the continuous work segments constrained by bottleneck resources, and perform break interval insertion adjustment based on the work segments. Based on the adjusted work segments, the membrane zone judgment is re-executed. If it is still within the membrane zone, the number of break interval insertions is adjusted until the membrane zone is resolved, thus completing the distribution network field operation planning.

[0039] The above-described solution of the present invention has at least the following beneficial effects:

[0040] By constructing a job coupling propagation chain based on perturbation vectors, jobs that were originally treated as equivalent only in terms of quantity and time are transformed into networked expressions with structural correlation and transmission attributes. This allows the implicit differences between jobs in terms of time, space, and resource dependence to be explicitly quantified, and the structural coupling propagation strength to participate in subsequent allocation decisions. This enables the structural differentiation of highly sensitive and highly dependent jobs in the pre-allocation stage, preventing them from being concentrated and superimposed under the equilibrium allocation mechanism. In turn, it blocks the evolutionary path of quantity equilibrium masking structural fragility, and shifts job allocation from apparent equilibrium to structural stability orientation.

[0041] By propagating gradient-driven chain segmentation and perturbation response-based path rearrangement mechanisms, the continuous, highly coupled structure formed during path compression is decomposed into sub-path units with structural separability. This breaks the cumulative amplification effect of coupling relationships in continuous paths and identifies and isolates potential fracture triggering zones during path generation. As a result, the path no longer exhibits a locally continuous and efficient but overall unstable structural form, thereby suppressing the problems of local resource aggregation and structural linkage instability induced by continuous path compression.

[0042] By elevating resources from static supply entities to dynamic variables participating in the propagation of propagation chain constraints, and quantifying their adsorption effect in the path using structural influence and locking index, the resource occupancy status in a local area can be interpreted as structural suppression of the propagation chain's extension capability. Furthermore, by implementing progressive fracture intervention in highly locked sections, the strong coupling relationship between resources and operations is gradually relaxed along the propagation path, ultimately achieving a structural transformation from continuous local resource coverage to cross-regional discrete and reversible structures, breaking the closed adsorption state of resources in space. Attached Figure Description

[0043] Figure 1 This is a system structure block diagram of the present invention;

[0044] Figure 2 This is a diagram of the propagation chain generation module of the present invention;

[0045] Figure 3 This is a diagram of the allocation module of the present invention;

[0046] Figure 4 This is a risk module diagram of the present invention;

[0047] Figure 5 This is a diagram of the resource resolution module of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] like Figures 1 to 5 As shown, embodiments of the present invention provide a full-process planning and resource collaborative management system for distribution network field operations, including:

[0050] The propagation chain generation module is used to acquire the raw data of field operations in the distribution network coverage area, and obtain the propagation chain after operation coupling propagation analysis;

[0051] The allocation module is used to identify candidate allocation objects according to the propagation chain, analyze the load differences between distribution network field teams, and obtain the work redistribution results;

[0052] The risk module is used to identify path structure sensitivity to lock in the areas to be allocated, form path rearrangement results with structural separability, and analyze the support of distribution network field operation resources to obtain imbalance areas.

[0053] The resource resolution module is used to analyze the impact and extended constraints between resources and operations through imbalance zone analysis, in order to identify and resolve membrane zones and complete the on-site operation planning of the distribution network.

[0054] In practice, the original data such as operation locations, time and space windows, and resource requirements within the distribution network coverage area are first collected through the propagation chain generation module. Based on the operation coupling and disturbance propagation characteristics analysis, the coupled propagation chain is obtained, which can accurately depict the relationship between operations. This not only realizes the structured expression of the implicit dependency relationship between operations, but also achieves the goal of transforming discrete operations into an analyzable chain structure.

[0055] Next, the allocation module identifies candidate allocation objects based on the propagation chain, and performs supply and demand matching and cross-team adjustment in combination with the team's carrying capacity field to obtain the work redistribution result. This ensures that the work allocation among teams is reasonable and balanced, reduces the risk of overall work chain disturbance, and avoids the concentrated distribution of highly correlated work.

[0056] The risk module generates paths for the assigned job sequences and calculates the disturbance response values ​​of each node in the path based on the disturbance propagation model. It identifies structurally sensitive sections and rearranges the paths by inserting time intervals or spatial transition nodes. This achieves both early identification of potentially unstable structures and the transformation of continuous coupled paths into separable structures, thereby improving the stability of the path structure.

[0057] Finally, the resource resolution module analyzes the degree of constraint of bottleneck resources on operations within the imbalance zone, calculates the blocking index to identify the membrane zone, and iteratively inserts fracture intervals in the continuously constrained operation segment until the membrane zone is resolved. This collaborative design, starting from both structural stability control and resource dispersal, further reverses the cognitive bias of existing technologies that misjudge the continuous accumulation of local resources as global efficiency. It not only ensures the anti-disturbance capability of the path structure, but also restores the discrete mobility of resources and the cross-regional emergency replenishment capability. It constructs a closed-loop management and control system of risk identification-structural optimization-membrane resolution, and achieves the coordinated unity of safety, efficiency and robustness in distribution network operation planning.

[0058] In a preferred embodiment of the present invention, the propagation chain generation module includes: a data acquisition unit, used to acquire the distribution network coverage area and divide it into several grids, such as 1km×1km, to acquire raw data of the distribution network field operation, including the type of power distribution equipment, coordinates of the operation point, feeder affiliation, planned time window, operation duration, participating team type, and required resource type, wherein the required resource type includes live-line working vehicles, insulating tools, monitoring personnel, and traffic coordination resources; the planned time window refers to the time period during which the operation is allowed to be executed; the participating team type includes live-line working teams, maintenance teams, emergency repair teams, operation and maintenance teams, etc.

[0059] The job disturbance unit is used to parse the execution environment dependency of each job based on the original data. It is used to transform the environmental constraints implicit in the job into a recognizable structure, and obtain the disturbance vector of each job after parameterized mapping, including environmental disturbance sub-vector, resource disturbance sub-vector and topology disturbance sub-vector.

[0060] The perturbation vector is a multi-dimensional structural expression formed by uniformly quantifying the time constraints, spatial constraints, resource dependencies, and structural relationships encountered by a task during execution. Specifically, the environmental perturbation sub-vector describes the degree to which a task is constrained by the external environment, and its internal features include the probability of weather disturbances, the availability coefficient of power outage windows, and the degree of time constraint; the resource perturbation sub-vector describes the pressure that a task places on the resource system, and its internal features include vehicle demand density, tool demand density, personnel demand intensity, and resource conflict probability; the topological perturbation sub-vector describes the position and connectivity of a task within the overall structure, and its internal features include grid distance density, adjacency strength, and topological position weight.

[0061] Among them, the probability of meteorological disturbance is obtained from historical meteorological frequency statistics, reflecting the degree to which the operation is affected by the weather; the power outage window availability coefficient is the ratio of the planned power outage period to the available time, reflecting the time margin of the operation; the time constraint is the ratio of the power outage permit period to the operation duration, reflecting the degree of operation time compression; and the vehicle demand density is the distribution of the number of vehicles used per unit time, reflecting the vehicle occupancy pressure.

[0062] Tool demand density is the frequency of tool use, reflecting the degree of tool shortage; personnel demand intensity is the ratio of actual number of personnel to standard configuration, reflecting the level of personnel workload; standard configuration refers to the minimum personnel and resource configuration required to complete a certain type of work under the guidance of specifications or experience; resource conflict probability is the probability of resource competition caused by overlapping work times, reflecting the risk of resource conflict; grid distance density is the average distance between adjacent work points, reflecting the spatial distribution density.

[0063] Adjacency strength is the number of adjacent work points within a statistical unit area, reflecting the degree of spatial connectivity; topological position weight is a positional encoding based on the node's position in the structure, reflecting the importance of the structure.

[0064] The chain parsing unit is used to analyze the operation coupling propagation characteristics based on the perturbation vector to obtain the propagation chain, including:

[0065] Norm normalization is performed on each sub-vector within the disturbance vector to convert it into a standardized vector representation, thereby eliminating the dimensional differences between variables of different dimensions. The similarity between nodes is calculated through the vector inner product to obtain the coupling strength matrix, whose elements represent the coupling strength between operations. This value describes the degree of interdependence between two operations in terms of structure, time, space, resources, and topology, and is the core basis for judging whether disturbances, risks, and constraints will be transmitted in a chain between operations. Similarity is calculated using cosine similarity.

[0066] The coupling strength matrix is ​​a combination of the coupling strengths between all jobs, used to describe the dependency network between global jobs;

[0067] The information entropy of each feature within the perturbation vector is calculated based on its dispersion in the global task, and the contribution coefficient of each feature is derived using the entropy weight method to obtain the structural coupling propagation strength. This strength is a comprehensive importance score calculated for a single task, which is the result of multiplying each feature by its corresponding contribution coefficient and summing the results. It is used to measure the propagation influence of the task in the entire coupled network. The greater the influence, the easier it is to trigger chain problems or chain propagation.

[0068] Information entropy is used to measure the dispersion of a feature across all jobs, and its formula is: ,in For the first The larger the information entropy of a feature, the more disordered its distribution, the stronger the feature's discriminative power, and the higher its weight should be. The total number of assignments. For the first One characteristic in the task Normalized weighting; It is the natural logarithm; For job indexing, For feature index; For the first One characteristic in the task The specific values ​​that can be taken on;

[0069] Calculate the difference coefficient based on information entropy. To calculate the contribution coefficient ,in The coefficient of variation; The contribution coefficient is used to assign weights to each feature, clarifying the importance weights of different features; The total number of features;

[0070] Based on the structural coupling propagation strength, an initial set is obtained, which includes multiple sets of initial nodes. By considering the numerical values ​​of the corresponding rows in the coupling strength matrix and the spatiotemporal constraints in the original data for each initial node, an initial propagation path is established. This path is a task transmission chain that satisfies the constraints of time, space, and coupling strength. It is used to describe how dependencies, disturbances, or risks are transmitted between tasks and serves as the basic structure for subsequent team allocation, path generation, and resource scheduling.

[0071] In practice, all tasks are first sorted according to the structural coupling propagation strength, and nodes with high identifier values ​​are selected as the starting set. For each starting node, subsequent nodes with high coupling strength are selected in descending order of the corresponding row value in the coupling strength matrix, and directed connections are established. During the connection process, the time windows of subsequent nodes are required to have executable overlap or sequential relationships with the preceding nodes, while also satisfying spatial reachability constraints, i.e., conforming to distribution network grid, topology, or geographical constraints, thereby ensuring that path construction has an executable basis. The node connections are gradually expanded in this way to form the initial propagation path.

[0072] Among them, the Top-N method or the mean-standard deviation method can be used to select key operations with greater influence and easier to trigger chain propagation, as the starting point of the propagation chain, i.e., the starting node.

[0073] The initial propagation path is deduplicated and merged to obtain the propagation chain and its propagation gradient.

[0074] In practice, if two paths share the same task (intersecting or repeating nodes), the two paths are considered to belong to the same dependency propagation system. They are spliced ​​together using the common node as the connection point and reordered according to time sequence and coupling strength. The directed order is then reorganized to obtain a complete propagation chain. This chain is formed by deduplication, splicing, and reconstruction of multiple initial scattered paths, resulting in several non-repeating, non-intersecting, and structurally complete task dependency chains.

[0075] The propagation gradient is the cumulative result of the difference in the propagation strength of the structural coupling of all adjacent nodes in the propagation chain. It reflects the overall trend of the change in the operation coupling strength and provides the core basis for subsequent propagation chain segmentation.

[0076] The propagation chain generation module transforms job environment dependencies, resource constraints, and topological relationships into perturbation vectors and constructs propagation chains based on information entropy and coupling strength. This replaces the original method of balancing allocation based solely on the number of jobs and working hours with a differentiated characterization based on structural influence. As a result, the potential sensitivity and transmission relationship between jobs are identified and embedded in the structure during the generation stage, thereby preventing multiple highly dependent and highly sensitive jobs from being misjudged as equivalent jobs before allocation.

[0077] Simultaneously, by constructing propagation chains and gradients, the changing trends of coupling strength within continuous paths are explicitly expressed, providing a basis for identifying locally compressed but structurally highly coupled work segments. This weakens the tendency for continuous resource aggregation caused by path compression at its source. This method can distinguish the differences in propagation impact within the chain structure, preventing tasks from clustering with other similar sensitive tasks due to quantity equilibrium. This allows for the early interruption of potential vulnerable clusters and resource membrane evolution paths during subsequent allocation and path organization.

[0078] In a preferred embodiment of the present invention, the allocation module includes: a bearing unit, used to take the structural coupling propagation strength as the demand strength, map the demand strength to the grid and combine it with the team's resource capacity to model the supply-demand ratio, so as to obtain the bearing capacity field of the team in the spatiotemporal dimension;

[0079] The higher the structural coupling propagation strength value, the more critical the task and the higher the resource demand (critical tasks require more resource support). Therefore, it is directly regarded as the resource demand intensity of the task to quantify the task's demand on the team's carrying capacity.

[0080] In practice, the structural coupling propagation strength of each task is regarded as the demand intensity, and it is mapped to the network coverage area according to the spatial location of the task. The demand intensities of all tasks in the same grid are superimposed to form a demand density function. At the same time, based on the team's movement speed, resource quantity, and available service time window in the grid, the resource supply capacity function of the team in the grid is calculated. By calculating the ratio of the supply function to the demand function, the carrying capacity function of the team in different spatial and temporal dimensions is obtained, and it is discretized into a grid-time two-dimensional matrix to obtain the carrying capacity field. This field is essentially the set of carrying capacities of the team in different spatiotemporal dimensions, which facilitates subsequent traversal analysis of the carrying capacity limit of the team in different regions and at different times, and establishes the mapping relationship between the team and the task.

[0081] Subsequently, the carrying capacity of each shift in different regions and time slots was analyzed, its upper limit and capacity fluctuation range were recorded, and a mapping relationship between the shift carrying capacity and the propagation chain nodes was established, thereby determining the carrying capacity of each operation under different shifts.

[0082] The demand density function describes the intensity of the total demand for all tasks in a given grid at a given moment. It is the ratio of the total demand for tasks within the grid to the area of ​​the region. It reflects the congestion level of tasks within the grid and the urgency of resource demand. The larger the value, the more tasks and the stronger the demand in the corresponding grid at a given moment, requiring more work team resources to cover the area. This provides a basis for judging whether a work team can handle the tasks in that grid.

[0083] The resource supply capacity function describes the resource support capacity that a corresponding work team can provide in each grid and at each time. It quantifies the work team's supply potential for grid operations and is obtained by multiplying the work team's operational efficiency by the available service time window. It reflects the resource abundance of the work team in a specific area and at a specific time. The larger the value, the more resources the work team can provide in the corresponding grid and at the corresponding time, and the stronger its ability to carry out operations. Corresponding to the demand density function, it is used to calculate the work team's carrying capacity and determine whether the work team can meet the operational needs of the grid.

[0084] The available service time window is the actual time that a work team can put into operation in the corresponding grid. The work team's work efficiency comes from the maximum work processing capacity per unit time as statistically analyzed from historical work execution records.

[0085] The carrying capacity function describes the upper limit of the work demand that a work team can meet at each grid at each time. The larger the value, the stronger the carrying capacity of the work team and the better it can meet the work demand of the grid. When the demand density function is 0, the carrying capacity function value must be greater than 1, indicating that the work team has no demand pressure in the grid and its carrying capacity is sufficient.

[0086] The candidate allocation unit is used to obtain a set of sub-chain segments by performing gradient-driven operation path segmentation on the propagation chain and its propagation gradient, and to perform decentralized allocation of work group scheduling under the constraints of the carrying capacity field to obtain candidate allocation objects. These objects are used to initially screen work groups that meet the requirements of spatiotemporal reachability and resource carrying capacity to ensure allocation feasibility.

[0087] The allocation result unit is used to limit the candidate allocation objects according to the same chain dispersion constraint principle, and perform consistency verification on the limited candidate allocation objects to obtain the allocation results of each sub-chain segment. This content is used to ensure that the work allocation meets the team carrying capacity constraints, so as to ensure that the work load of each team does not exceed the limit, the work distribution is not concentrated, and the time and airing schedule does not conflict. This allocation is a static matching process, which mainly focuses on the feasibility verification of the team's carrying capacity, without considering the anti-disturbance characteristics of the path structure and the chain transmission risk after the disturbance.

[0088] In practice, when the gradient difference between adjacent nodes in the propagation chain exceeds the preset gradient threshold, it indicates that there is a significant discontinuity or drastic change. This position is taken as the chain splitting point, thereby splitting the original propagation chain into multiple sub-chain segments with stable gradients and mild changes in coupling strength. This avoids unreasonable subsequent team allocation and risk accumulation due to a sudden change in coupling strength in a chain, and also facilitates accurate matching of team capabilities.

[0089] After the sub-segment division is completed, the shift allocation operation is performed on each sub-segment. During the allocation process, the carrying capacity field is used as a constraint condition to match the spatial location and time window of the sub-segment one by one. That is, the grid of the sub-segment must be covered by the shift and spatially accessible; the planned time window of the sub-segment must coincide with or match the serviceable time window of the shift.

[0090] Simultaneously, work groups that satisfy the carrying capacity function greater than 1 are selected as candidate allocation objects; in the candidate work groups, the same chain dispersion constraint is further introduced, that is, different sub-chain segments of the same propagation chain are restricted from being allocated to the same work group, thereby avoiding the accumulation of propagation risk in a single work group and the paralysis of the entire propagation chain; through traversal matching and constraint screening, the initial allocation result of each sub-chain segment is determined.

[0091] After initial allocation, the set of sub-chain segments carried by each shift is statistically analyzed to determine the relationship between total demand intensity and carrying capacity, and to detect any overloading or time conflicts. Simultaneously, it checks for concentrated allocation of sub-chain segments within the same propagation chain. If a violation of the dispersion constraint is found, a re-matching operation is performed, migrating some sub-chain segments to other shifts that meet the conditions. After verification, the allocation results of sub-chain segments that satisfy both carrying and dispersion constraints are output. This allocation result represents the final correspondence between sub-chain segments and executing shifts, clarifying the execution entity, implementing the dispersion constraint within the same chain, avoiding risk aggregation and shift overload, and providing a stable allocation basis for subsequent path planning and resource management.

[0092] Gradient difference refers to the difference in the propagation strength of structural coupling between adjacent nodes;

[0093] The redistribution unit, based on the redistribution results, identifies differences in workload among work groups and combines these differences with work relationships to perform cross-work group adjustments, thus generating a redistribution result. The goal of this unit is to make the workload of all work groups more even and balanced.

[0094] In practice, the structural coupling propagation strength of all work nodes carried by each work group is accumulated to obtain the total load value of the work group, and the average load value of all work groups is calculated. Then, the load offset of each work group is calculated to measure the degree of deviation of the work group from the overall load level.

[0095] Prioritize selecting tasks (starting nodes) in the work group whose load offset values ​​exceed the preset offset threshold and are located at critical positions in the propagation chain as adjustment targets. Combine the remaining carrying capacity of other work groups and their spatial location relationships to determine the target work groups that can receive the tasks. During the adjustment process, the task execution time constraints and resource matching conditions are met at the same time, and the original task association relationships are kept from clustering, thereby realizing the redistribution of tasks among different work groups.

[0096] Job relationships include at least the coupling, dependency, and belonging to the same propagation chain between jobs;

[0097] The result of job reallocation is a job allocation structure that satisfies resource load balancing, same-chain dispersion constraints, and no time and space conflicts. It is used to reflect which jobs each team needs to perform and their execution order.

[0098] The allocation module replaces the traditional number of jobs with the structural coupling propagation strength as the basis for demand characterization, and constructs a carrying capacity field to achieve a fine match between supply and demand in the spatiotemporal dimension. This allows the job allocation to introduce structural difference constraints at the generation stage, thereby avoiding the equal allocation of multiple jobs that are sensitive to external conditions and have a high degree of propagation impact as equivalent jobs, thus weakening the formation path of fragile node clusters.

[0099] Meanwhile, by using chain segmentation driven by propagation chain gradient and co-chain dispersion constraints, highly coupled work segments that might have been continuously compressed in space and time are structurally disassembled and dispersed across shifts. This prevents resources from forming continuous coverage along a single path, instead maintaining a separable carrying state among multiple shifts, thereby suppressing membrane evolution caused by continuous adsorption of resources in local areas. Furthermore, through subsequent redistribution based on load offset, the resource aggregation trend caused by local supply and demand imbalances is further broken up, thus avoiding a structural imbalance where resources accumulate continuously in a single area and are difficult to allocate to surrounding areas.

[0100] In a preferred embodiment of the present invention, the risk module includes: a planning unit, used to generate a job planning sequence based on the job redistribution results and the original data;

[0101] Specifically, firstly, a preliminary sorting is performed according to the planned time window of the tasks, and tasks with overlapping or adjacent time intervals are clustered to form a set of time clusters. Within each time cluster, an adjacency sorting is performed based on the spatial grid coordinates of the task points. By calculating the grid distance between nodes and the path travel cost, the node order is fine-tuned to ensure that the sequence has spatial continuity while meeting time constraints. Through the above two-level sorting mechanism, the initial path node sequence of each shift is formed; the nodes are the task points.

[0102] Next, the transfer cost is calculated for adjacent nodes in the sequence. Specifically, the spatial distance between nodes is read, and the average moving speed of the vehicles configured in the shift is combined to calculate the time taken to move between nodes.

[0103] Finally, the path structure of each shift is mapped to the distribution network spatial grid to obtain the operation planning sequence. This sequence is a set of operation paths generated for each shift in the execution order after time clustering, spatial adjacency sorting and transfer cost calculation based on the operation redistribution results. It is used to transform the allocation results into operation routes that can be executed on site, and at the same time provides a standardized path basis for subsequent disturbance response analysis, structural vulnerability identification and resource imbalance judgment.

[0104] The disturbance analysis unit is used to perform disturbance response propagation analysis on the planned path based on the operation planning sequence, combined with the coupling relationship between operations and the propagation strength. It identifies disturbance response results that reflect the structural sensitivity of the path. The results describe the distribution of disturbance sensitivity on the entire operation path. Through sequence difference analysis, it identifies the locations of sudden increases in structural changes, which is used to locate potential unstable points and lock the area to be allocated, which includes at least one fracture trigger node. This content considers the vulnerability of the structure to diagnose risks and locate vulnerable sections.

[0105] The planned path refers to the order in which tasks are executed within the planned task sequence;

[0106] The disturbance response results include disturbance response values ​​from multiple operations. The formula for calculating the disturbance response values ​​is as follows: ,in For homework The disturbance response value reflects the overall sensitivity of a node to disturbances from surrounding nodes, in order to identify vulnerable points in the path. The larger the value, the more sensitive, vulnerable, and critical the node is.

[0107] For the purpose of the work The set of adjacent nodes centered on the task, i.e., the tasks in the task planning sequence. The preceding and following nodes focus on their direct impact on the actual execution path, avoiding interference from irrelevant nodes.

[0108] The coupling strength between adjacent operations is the strength of the coupling. The stronger the coupling, the easier it is for disturbances to escape from the operation. Transmission to work This reflects the degree of difficulty in transmission;

[0109] For homework The structural coupling propagation strength, i.e., the operation The greater the value of the value of the disturbance's criticality and its ability to spread, the more significant the impact of the disturbance on the operation. A stronger impact is generated; conversely, the smaller the value, the limited the impact even if problems occur. Therefore, for operations... The smaller the impact, the more powerful the source of the disturbance becomes.

[0110] and For job indexing, ;

[0111] When the disturbance response value exceeds the preset response threshold, it indicates that this path is very fragile, very sensitive, and extremely susceptible to breakage due to disturbances. The operation is then recorded as a breakage trigger node, and the corresponding segment is recorded as a pending allocation area, i.e., the operation segment with excessively high disturbance response, unstable structure, and inability to be executed normally. As a direct indicator of the path structure's fragility, the breakage trigger node accurately locates the key node in the entire path that is most likely to fail due to disturbances and cause path interruption, providing anchor points for subsequent breakage interval insertion. It is the core operation object for path structure optimization and improving separability.

[0112] The adjustment unit is used to perform break interval insertion adjustment on the area to be allocated, forming a path rearrangement result with structural separability. This operation aims to break down the original fragile and continuous path structure that is prone to cascading failures into multiple independent and unaffected sub-path structures through insertion adjustment.

[0113] The stability and failure propagation risk of the path structure are quantitatively characterized by the disturbance response value. The resulting path rearrangement results can be used to set up isolation intervals in advance and actively avoid structural solidification, thereby achieving pre-emptive prevention and control of membrane formation risk and effectively reducing subsequent multi-regional and high-frequency membrane formation phenomena.

[0114] Specifically, by combining the location of the fracture trigger node in the path and the disturbance response of adjacent nodes, it is determined whether it constitutes a continuous fracture interval, that is, an area where multiple adjacent nodes meet the fracture conditions; the continuous fracture interval is marked as a whole, and its start position, end position and the set of operation nodes involved are recorded. The fracture interval is divided according to the size of the disturbance response value, and the node with the largest relative response value is used as the dividing boundary to split the original path into multiple sub-path segments.

[0115] Break intervals are inserted between adjacent sub-path segments. Break intervals are divided into two categories: time intervals and spatial transitions. Time intervals are created by adjusting the job execution time to form non-overlapping time windows between adjacent sub-path segments. Spatial transitions are created by introducing virtual transition nodes, which, if there are no job nodes, only represent spatial buffer positions, increasing the spatial distance between path segments. During the insertion process, the job execution order is kept intact, while ensuring that the pre-constraint relationships still hold.

[0116] After the break insertion is completed, the reconstruction process is performed on all the split sub-path segments, and the path connectivity and time feasibility are verified to ensure that each sub-path segment can still form a valid execution path in an independent case. Then, the sub-path segments are reconnected according to the original path logical order to form a complete path structure with break markers, and the sub-path segment number and break interval information of each node in the path are recorded. Finally, the set of broken reconstruction paths and its additional structure identifiers are output.

[0117] Both temporal and spatial adjustments aim to make the reconstructed path structurally separable. Separability means that the path is broken into controllable small blocks, and the break of any block will not affect the whole. The result of the path rearrangement is an executable path structure containing sub-path segments, break intervals, and connectivity information. Its function is to eliminate the risk of continuous structure and provide a stable and executable path structure for subsequent steps.

[0118] The resource data unit is used to obtain resource distribution results by correlating the resource usage of each job with the execution order of the path based on the path reordering results;

[0119] In practice, based on the path rearrangement results, the resource types required for each task are matched one by one. During the matching process, according to the resource requirement set of the task record, the task vehicles, tools and personnel resources are bound to the corresponding nodes one by one, and the start time and release time of each resource in the task are specified. The start time of occupation is taken as the start time of the task plan, and the release time is taken as the end time of the task plan.

[0120] Simultaneously, the migration relationship of resources between path nodes is recorded, that is, the time interval and spatial path of resources from the current node to the next node, so as to obtain the resource distribution result. The result specifically includes the resources bound to each job, the occupation and release time of each resource and the grid position, which is used to quantify the occupation and migration rules of resources in the spatiotemporal dimension and provide the underlying network structure for the resource reversible field.

[0121] The flow analysis unit is used to determine the ability of resources within the grid to continue supporting subsequent operations based on resource distribution results, obtain resource allocation results, and identify allocation-restricted areas; these restricted areas provide key adjustment targets for subsequent dynamic resource scheduling, path optimization, and the construction of reusable fields.

[0122] In practice, the transfer behavior of resources between path nodes is first extracted to obtain the transfer time cost. Then, the structural coupling propagation strength is introduced to modify the resource flow. The structural coupling propagation strength value of the current operation node of the resource is used as an influencing factor. The transfer capability is modulated through an exponential decay function so that nodes with high coupling strength inhibit the resource flow. On this basis, a reversibility index is constructed.

[0123] Using the grid as the basic unit, the average reversibility index of all resources in each grid within the current time slice is calculated. If the average reversibility index is lower than the preset reversibility threshold, it indicates that the overall resource flow efficiency is insufficient and the spatial migration mobility is limited. This is recorded as a restricted allocation area.

[0124] Here, "subsequent jobs" refers to all unexecuted jobs following the current job in the path reordering result.

[0125] The resource allocation result is a set of reversible structural parameters of resources, including the reversibility index of each resource, the feasibility status of resource transfer, and the supply and demand status of resources in each grid. It is used to determine whether the resources can support subsequent operations and to provide input for the next step of membrane determination.

[0126] The formula for calculating the reversibility index is as follows: ,in It is a reversibility index; the higher the value, the more flexible and easier the resources are to recover. It is used to quantify resource liquidity, assess support capacity, and identify allocation bottlenecks. The time cost of transferring resources is the sum of the time spent moving the resources and the time spent releasing them. It is used to characterize the efficiency of resource transfer in the time and space dimensions. The higher the transfer time cost, the lower the resource transfer efficiency. is a natural constant, with a value of approximately 2.71828;

[0127] It is an exponential decay function, which is an exponential decay term used to characterize the inhibitory effect of structural coupling propagation on resource flow. The stronger the structural coupling propagation, the stronger the inhibition on resource flow, and the more difficult it is for resources to be extracted and returned. The smaller the value, the more it suppresses liquidity; from a structural perspective, it reflects the lock-up effect of highly coupled nodes on resource flow. The suppression strength resulting from coupling; The structural coupling propagation strength;

[0128] The suppression coefficient ranges from 0 to 1, and its specific value can be determined based on historical operation data. This involves statistically analyzing multiple batches of historical distribution network field operations to determine the degree of resource flow restriction under different structural coupling propagation intensities. Through multiple simulation iterations, a value that can stably and accurately identify the restricted allocation area is selected as the suppression coefficient. During system initialization, commonly used default values ​​from engineering projects can be directly adopted, such as... =0.5, which meets the resource liquidity analysis requirements in general distribution network operation scenarios.

[0129] The risk analysis unit is used to analyze the mismatch between the distribution of tasks and the resource availability within the restricted allocation area, based on the results of allocation restriction and path rearrangement. This results in a local resource imbalance and a resource constraint structure. This aspect considers whether resources can flow between tasks, be reused, and dynamically support subsequent tasks; it pertains to dynamic flow.

[0130] Specifically, by extracting the job nodes corresponding to all fractured reconstruction paths within the restricted allocation area, the core information of each job is clarified, including job location, required resource type and quantity, and planned execution time window; at the same time, information on all allocated resources within the area is extracted, including resource type, currently bound job, reversibility index, and release time.

[0131] Regarding total quantity matching verification: By resource type, the total amount of resources that can be allocated within the restricted area is counted, that is, the sum of all resources whose reversibility index is not lower than the preset reversibility threshold. Resources with insufficient circulation efficiency are excluded. Then, by resource type, the total resource demand of all operations within the restricted area is counted. By comparing the two, if the total amount of resources that can be allocated is less than the total resource demand, it is determined that there is a mismatch; otherwise, the next step of verification is carried out.

[0132] Regarding time matching verification: For each job's time window, match its corresponding resource release time; if a resource is released but cannot reach the next job node on time, causing the next job to fail to start on time due to lack of resources, it is judged as a mismatch; otherwise, proceed to the next verification step.

[0133] In terms of spatial matching verification: the specific locations of the jobs within the grid within the restricted area are grouped, and the number of jobs and resource requirements within the specific locations are counted; if the job and resource locations do not match within the same grid, it is judged as a mismatch;

[0134] When a mismatch is determined, it is considered a state of local resource imbalance. This state refers to a situation where the resource supply capacity at a certain location within the restricted area does not match the demand intensity and execution rhythm corresponding to the operation distribution at that location, resulting in resources being unable to effectively support operation execution or resources being idle and wasted. It is a specific quantitative description of the contradiction between local resources and operations within the restricted area.

[0135] The essence of resource constraint structures is to transform the contradiction of local resource imbalance into executable constraints, preventing resource imbalance from recurring after adjustments. Specifically, corresponding constraint rules are formulated for different types of imbalance: Regions with insufficient resource supply: No new operations are allowed in the constrained region; some existing operations must be transferred to regions with sufficient resources. Regions with weak resource mobility (returnability index below a preset return threshold): Constrain the time and path of resource transfers in and out to avoid exacerbating resource imbalance. Regions with idle resources: The constrained region must accept operations transferred from imbalanced regions to improve resource utilization. Resource idleness includes at least the spatial and temporal gaps caused by break intervals. Here, some existing operations are reconfigurable operations, specifically those that can be transferred without disrupting the overall execution order and are not part of the starting node.

[0136] The extraction unit is used to aggregate spatially adjacent locations where local resource imbalance occurs to obtain imbalance zones, and then extract the available resources and available operations within the imbalance zones in combination with the resource constraint structure.

[0137] An imbalance zone is the result of aggregating consecutive segments of spatially adjacent work locations that exhibit local resource imbalances, according to the execution order after path rearrangement.

[0138] The risk module introduces disturbance response propagation during the path generation stage to quantify the structural sensitivity of each node in the work sequence and identify continuous high-sensitivity segments. This allows work sequences that were originally considered continuous and efficient during path compression to expose their potential linkage instability characteristics before execution. This avoids keeping structurally highly coupled and sensitive work segments in a continuous arrangement, thereby interrupting the evolutionary path of continuous path compression—strengthened structural coupling—amplified overall instability.

[0139] Meanwhile, by using the break interval insertion mechanism, the continuous path is decomposed into sub-paths with structural separability, so that the disturbance of a single segment no longer spreads along the path, and the pulling effect of the local continuous cluster structure on the overall execution stability is weakened.

[0140] By further combining resource distribution and reversibility index modeling, the occupancy and flow capacity of resources in the path are jointly analyzed, so that resources are transformed from being continuously bound along the path to being reversible and allocable across segments, thereby avoiding the membrane state in which resources form continuous coverage in local areas while voids appear in external areas.

[0141] In a preferred embodiment of the present invention, the resource resolution module includes: an influence unit, used to analyze the influence propagation process of different adjustable resources on different operations in each path rearrangement result based on adjustable resources, adjustable jobs, and path rearrangement results, and to obtain the structural influence degree corresponding to each adjustable resource; the analysis here focuses on adjustable resources and adjustable jobs;

[0142] In practice, the system focuses on available resources and considers different available jobs in the path rearrangement results as affected nodes. The reversibility index is used as a resource characteristic variable, and the structural coupling propagation strength is used as a job response variable. The correlation strength between the resource characteristic variable and the job response variable is calculated by using the Pearson correlation coefficient algorithm. The larger the absolute value of the correlation strength, the stronger the correlation between the resource node characteristics and the job node response, which indirectly reflects the greater the potential influence of the resource node on the job node.

[0143] Subsequently, in order to transform the correlation strength into a directly interpretable degree of influence, i.e., the influence coefficient, and to eliminate the difference in the dimensions between resource characteristic variables and job node response variables, the influence coefficient of a single resource node on a single job node is obtained by combining parameters such as the mean and standard deviation of the two types of variables.

[0144] Next, along the execution direction of each job in the path rearrangement result, the influence coefficient of the resource node on each job node is calculated node by node to clarify the transmission law of the influence in the propagation chain. The higher the correlation strength and the better the dimensional adaptability, the larger the influence coefficient and the more obvious the propagation.

[0145] Finally, the influence coefficients of resource nodes on all available operational nodes in the imbalance zone are summed to obtain the comprehensive influence degree of the resource node on the path in the imbalance zone, that is, the structural influence degree corresponding to the resource node. The relevant formula is: ,in For resources The structural impact on all adjustable operations in the imbalance zone is greater than or equal to 0. The larger the value, the more resources... The greater the actual impact on all deployable operations; For resources With homework The absolute value of the correlation strength between them;

[0146] The standard deviation of the task response variable represents the global dispersion, which is used to differentiate the importance of different tasks.

[0147] The standard deviation of resource characteristic variables is used to normalize the scale differences of resources themselves, so as to avoid the fact that a large quantity of resources will naturally have a greater impact.

[0148] It is a normalization coefficient used to eliminate differences in dimensions and balance the weights between tasks and resources; For resource indexing, For job indexing, This represents the total number of operations that can be allocated within the imbalance zone. This is the influence coefficient;

[0149] Simultaneously, the path range and maximum impact location of the diffusion effect are recorded for spatial structure identification in subsequent membrane determination.

[0150] The extended constraint unit is used to extract the available resources with the greatest structural impact as bottleneck resources. Based on the bottleneck resources and structural impact, the degree of constraint of the bottleneck resources on the propagation link extension capability is analyzed to obtain the locking index. If the locking index exceeds the preset locking threshold, it indicates that the propagation link is constrained by the bottleneck resources, the resource support efficiency for operation is insufficient, the link extension capability is limited, and the regional resource mobility fails. In this case, it is determined to be a membrane zone. Otherwise, no judgment is made. This step is used to check whether there are any missed cases that still form membrane zones.

[0151] In this invention, all parameters are dimensionless by using dimensionless processing technology to remove their dimensions; and all thresholds can be obtained by the mean-standard deviation method.

[0152] Based on the impact coefficient of each available resource on a single operation, the operations are traversed in the path rearrangement order. The maximum number of operations with a continuous impact coefficient lower than the preset intensity threshold is counted to obtain the actual extendable length, which is the number of continuous operations that can be freely extended without being constrained by bottleneck resources. At the same time, the total number of all available operation nodes in the imbalance zone is taken as the maximum extendable length. Then, the ratio of the actual extendable length to the maximum extendable length is calculated. By subtracting this ratio from 1, the blocking index is obtained. This index is a quantitative evaluation value of the degree of resource blockade on the propagation link, used to characterize the limited resource mobility and structural membrane risk in the region, thereby determining whether each region meets the membrane zone identification conditions.

[0153] The membrane zone is a local area defined by the structural constraints formed by bottleneck resources. This area exhibits closed characteristics at the functional level and has limited link extension capabilities.

[0154] It should be noted that even if the overall workload of the shift teams has been balanced, a single transmission path may still be blocked due to bottleneck resources. Therefore, it is necessary to carry out special solutions and optimization and control for the membrane zone in order to improve the cross-regional resource linkage and collaborative support capabilities.

[0155] The resource resolution module also includes:

[0156] The process control unit is used to rearrange the order along the path within the membrane zone, i.e., the path rearrangement result, extract the continuous work segments constrained by bottleneck resources, and perform break interval insertion adjustment based on the work segments. Based on the adjusted work segments, the membrane zone judgment is re-executed. If it is still within the membrane zone, the number of break interval insertions is adjusted until the membrane zone is resolved, and the distribution network field operation planning is completed.

[0157] A work segment continuously constrained by bottleneck resources refers to a work segment whose continuous impact coefficient is not lower than the preset intensity threshold.

[0158] Specifically, the first step is to perform a break interval insertion operation in the middle of the work segment: inserting a time interval or spatial transition can break the continuous strong correlation between bottleneck resources and operations, increasing the maximum number of operations with continuous influence coefficients below the threshold in the propagation path, increasing the actual extendable length, and reducing the locking index to below the preset locking threshold, thereby achieving complete elimination of the membrane zone. If the locking index still exceeds the preset locking threshold after adjustment, another break interval insertion operation is added, this time in a segmented, equally divided insertion manner, until the membrane zone is eliminated;

[0159] The break interval is divided into two categories: time interval and spatial transition. The time interval adjusts the job execution time to form a non-overlapping time window between adjacent sub-path segments. The spatial transition introduces virtual transition nodes, which, if there are no job nodes, only represent spatial buffer positions, increasing the spatial distance between path segments. During the insertion process, the job execution order is kept intact, while ensuring that the pre-constraint relationship still holds.

[0160] The resource resolution module couples resource rotatability with the strength of operation structure propagation and constructs structural influence degree with the cumulative result of influence coefficient propagation along the path. This makes resources no longer regarded as independent supply units, but reconstructed as constraint factors participating in the evolution of operation propagation chain. This can reveal the internal mechanism of resource transformation from schedulable state to structural adsorption state in the continuous compression process of the path.

[0161] Based on this, the propagation chain extension capability is reversed by using the lock-in index, so that the local continuous aggregation structure that was originally misjudged as efficient under the conditions of quantity balance and path compactness is re-identified as a lock-in structure dominated by bottleneck resources, thereby realizing the structural-level discrimination of the resource membrane formation process.

[0162] Furthermore, by progressively inserting fracture intervals into continuous high-impact operation segments, the highly correlated propagation chain between resources and operations is forcibly decoupled. This breaks the coupling relationship between the continuous adsorption of resources and the resource voids in the external area at the propagation path level, allowing resources to be reconstructed from a locally closed state to a cross-path reversible state, thus avoiding the reverse suppression of global allocation flexibility by local structural stability.

[0163] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A full-process planning and resource collaborative management system for distribution network field operations, characterized in that: The system includes a propagation chain generation module, an allocation module, a risk module, and a resource resolution module; The propagation chain generation module is used to obtain the raw data of the distribution network coverage area and obtain the propagation chain after operation coupling propagation analysis; The allocation module is used to identify candidate allocation objects according to the propagation chain, analyze the load differences between work groups, and obtain the job redistribution results. The risk module is used to identify path structure sensitivity to lock in the areas to be allocated, form path rearrangement results with structural separability, and analyze the support of distribution network field operation resources to obtain imbalance areas. The risk module includes a resource data unit, a flow analysis unit, a risk analysis unit, and an extraction unit; The resource data unit is used to obtain resource distribution results by correlating the resource usage of each job with the execution order of the path based on the path reordering results; The flow analysis unit is used to determine the ability of resources within the grid to continue supporting subsequent operations based on resource distribution results, obtain resource allocation results, and identify areas with limited allocation. The risk analysis unit is used to analyze the mismatch between the distribution of tasks and the resource acceptance relationship within the allocation-restricted area based on the allocation-restricted area and the path rearrangement results, to obtain the local resource imbalance state, and to form a resource constraint structure. The extraction unit is used to aggregate spatially adjacent locations that exhibit local resource imbalance to obtain imbalance zones, and, in conjunction with the resource constraint structure, extract the allocatable resources and allocatable operations within the imbalance zones; where the imbalance zone refers to spatially adjacent operation locations that exhibit local resource imbalance. The resource resolution module is used to analyze the impact and extended constraints between resources and operations through imbalance zones, in order to identify and resolve membrane zones and complete the on-site operation planning of the distribution network. The resource resolution module includes influence units and extended constraint units; The influence unit is used to analyze the propagation process of the influence of different available resources on different jobs in each path rearrangement result based on available resources, available jobs and path rearrangement results, and to obtain the structural influence degree corresponding to each available resource. The extended constraint unit is used to extract the adjustable resources with the greatest structural influence as bottleneck resources. Based on the bottleneck resources and structural influence, the constraint degree of the bottleneck resources on the propagation link extension capability is analyzed to obtain the locking index. If the locking index exceeds the preset locking threshold, it is determined to be a membrane region. The membrane region is a local area defined by the structural constraints formed by the bottleneck resources.

2. The full-process planning and resource collaborative management system for distribution network field operations as described in claim 1, characterized in that, The propagation chain generation module includes: The data acquisition unit is used to acquire the distribution network coverage area and divide it into several grids on an average basis to obtain the raw data of the distribution network field operation; The job disturbance unit is used to parse the environment dependency of each job execution based on the original data and obtain the disturbance vector after parameterized mapping. The chain parsing unit is used to analyze the operation coupling propagation characteristics based on the perturbation vector and obtain the propagation chain.

3. The full-process planning and resource collaborative management system for distribution network field operations according to claim 2, characterized in that, Based on the perturbation vector, the operation coupling propagation characteristics are analyzed to obtain the propagation chain, including: The similarity between nodes is calculated by vector inner product, and the coupling strength matrix is ​​obtained, whose inner elements are the coupling strength between tasks. The information entropy of each feature within the perturbation vector is calculated based on its degree of dispersion in the global operation, and the contribution coefficient of each feature is derived using the entropy weight method to obtain the structural coupling propagation strength. Based on the structural coupling propagation strength, an initial set is obtained, which includes multiple sets of initial nodes. For each initial node, an initial propagation path is established according to the numerical value of the corresponding row in the coupling strength matrix and the spatiotemporal constraints in the original data. The initial propagation path is deduplicated and merged to obtain the propagation chain and its propagation gradient.

4. The full-process planning and resource collaborative management system for distribution network field operations according to claim 3, characterized in that, The allocation module includes: The load-bearing unit is used to take the structural coupling propagation strength as the demand strength, and to map the demand strength to the grid and combine it with the team's resource capacity to model the supply-demand ratio, so as to obtain the team's load-bearing capacity field in the spatiotemporal dimension. The candidate allocation unit is used to obtain a set of sub-chain segments by performing gradient-driven operation path segmentation on the propagation chain and its propagation gradient, and to perform decentralized allocation of shift scheduling under the constraint of carrying capacity field to obtain candidate allocation objects. The allocation result unit is used to limit the candidate allocation objects according to the same chain dispersion constraint principle, and perform consistency verification on the limited candidate allocation objects to obtain the allocation results of each sub-chain segment; The redistribution unit is used to perform cross-shift adjustments based on the redistribution results by identifying differences in the workload of each shift and combining the work relationships, thereby generating work redistribution results.

5. The full-process planning and resource collaborative management system for distribution network field operations according to claim 4, characterized in that, The risk module also includes: Planning units are used to generate job planning sequences based on job redistribution results and raw data; The disturbance analysis unit is used to perform disturbance response propagation analysis on the planned path based on the job planning sequence, combined with the coupling relationship and propagation strength between jobs, to identify the disturbance response results, locate potential unstable points, and lock the area to be allocated, which includes at least one fracture trigger node. The adjustment unit is used to perform break interval insertion adjustment on the area to be allocated, resulting in path rearrangement.

6. The full-process planning and resource collaborative management system for distribution network field operations according to claim 5, characterized in that, The resource resolution module also includes: The process control unit is used to rearrange the order along the path within the membrane zone, extract the continuous work segments constrained by bottleneck resources, and perform break interval insertion adjustment based on the work segments. Based on the adjusted work segments, the membrane zone judgment is re-executed. If it is still within the membrane zone, the number of break interval insertions is adjusted until the membrane zone is resolved, thus completing the distribution network field operation planning.

Images