Multi-cabin collaborative dispatching method, medium and equipment for shared service cabins at power operation sites

Abstract

The present invention provides a method, medium, and equipment for collaborative dispatching multiple shared service cabins at power operation sites, relating to the field of power emergency repair technology. The method obtains basic information about all power operation sites and service cabin dispatch bases within a region, combines historical operation task information with weather forecast data, predicts predicted power emergency repair tasks that may occur within a set future time period within the region, and constructs a predicted task pool based on the predicted power emergency repair tasks and planned power operation tasks. When an operation demand is received from a power operation site within the region, the mobile service cabins are jointly dispatched based on the location information of the power operation site and the service cabin dispatch base, the predicted task pool, and traffic conditions. The method ensures that when the operation demand is a predicted power emergency repair task, the service cabin dispatch base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation demand. The above method can shorten the time required for emergency repairs and achieve the most timely power restoration possible.

Description

Technical Field

[0001] The present invention relates to the technical field of power emergency repair, and in particular to a method, medium and equipment for collaborative dispatching multiple shared service cabins at a power operation site. Background Art

[0002] With the advancement of urban-rural integration, urban and rural power supply services are gradually becoming integrated and converged. Management departments at all levels are increasingly demanding a high degree of timeliness in power supply repairs. In particular, in the face of sudden power outages, how to quickly complete repairs and restore power service has become a major challenge in current power supply services. Upon receiving a repair order, the power repair department uses a service cabin equipped with an onboard electrical tool cabinet to carry out the repair. Common and specialized electrical tools are transported to the site via the service cabin for operation. In the prior art, service cabin dispatch is limited to scheduled power operation orders. Since scheduled power operation orders are typically routine maintenance tasks with relatively low timeliness, dispatching one or more service cabins from the nearest dispatch base is sufficient to carry out the operation. However, sudden power outages due to various weather conditions often occur, leading to power supply interruptions. If a sudden power outage not included in the scheduled power operation order occurs and the nearest dispatch base is short of service cabins, dispatching service cabins from more distant service cabins increases repair time, hindering the prompt completion of the repair work and resulting in delayed power restoration. Summary of the Invention

[0003] The present invention aims to provide a method, medium, and device for collaboratively dispatching multiple shared service modules at a power operation site. This method aims to enable coordinated dispatch of service modules to ensure that, when a sudden power failure occurs due to weather, the nearest service module dispatch base has sufficient service modules to complete emergency repairs, shortening the repair time and enabling timely power restoration. The specific technical solution is as follows:

[0004] A method for collaboratively dispatching multiple shared service cabins at an electric power operation site, the method comprising the following steps:

[0005] S100, obtaining basic information of all power operation sites and service module dispatch bases in the region, including their location information and historical operation task information;

[0006] S200, combining historical operation task information and weather forecast data, predicting the predicted power emergency repair tasks that may occur in the region within a set period of time in the future, and building a predicted task pool by combining the predicted power emergency repair tasks and planned power operation tasks;

[0007] S300. When receiving an operation demand from a power operation site in the region, the mobile service cabin is jointly dispatched according to the location information of the power operation site and the service cabin dispatch base, the predicted task pool, and the traffic conditions, and it is ensured that when the operation demand is a predicted power repair task, the service cabin dispatch base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation demand.

[0008] Furthermore, step S100 includes the following steps: obtaining the location information of all power operation sites in the area; obtaining the historical operation task information of all power operation sites in the area in the past preset time period, including the time data of each task and the service cabin data required for each task; obtaining the hardware information of the service cabin dispatching base, including the number of mobile service cabins owned by the service cabin dispatching base and the location information of the service cabin dispatching base; obtaining the historical service information of the service cabin dispatching base, including the number and type of power operation tasks undertaken by the service cabin dispatching base in the past preset time period, the number of mobile service cabins dispatched for each task, and the mobile service cabin dispatching service time data.

[0009] Furthermore, the step S200 includes the following steps:

[0010] S210: Construct a power operation history task pool based on the locations of all power operation sites in the region, and the time points and durations of each operation task in the basic information;

[0011] S220. Divide the power operations in the historical task pool into planned operation tasks and emergency repair tasks based on the nature of the power operation tasks. Planned operation tasks are power operation tasks that are scheduled in advance, and emergency repair tasks are emergency repair tasks performed in response to sudden power failures.

[0012] S230, predicting possible power emergency repair tasks based on the emergency repair tasks in the historical task pool and the weather forecast data in the area;

[0013] S240, combining official data, collecting and extracting planned work tasks for a set period in the future within the region;

[0014] S250, comprehensively plan operation tasks and predict power emergency repair tasks to build a prediction task pool within a future set time period area.

[0015] Furthermore, step S230 includes the following steps:

[0016] S2310. Based on weather forecast data for the area during a set period in the future, determine the possibility of extreme weather in the area during the set period in the future. The extreme weather is divided into two categories: Category 1 extreme weather refers to strong winds, heavy rain, or heavy snow that directly damage power supply facilities; Category 2 extreme weather refers to excessively high or low temperatures that indirectly damage power supply facilities due to excessive power load.

[0017] S2320: Acquire weather information for a preset period in the past within the region, further divide the power operation history task pool, and extract power operation history tasks caused by extreme weather type 1 and extreme weather type 2;

[0018] S2330: Divide the area into grids of a set size, and count the historical power operation tasks caused by Category 1 extreme weather and Category 2 extreme weather that occurred in each grid within a preset period of time. If each occurrence occurs once, the grid count is incremented by 1; the Category 1 and Category 2 values ​​of all grids in the area can be obtained.

[0019] S2340, based on the number of occurrences of Category 1 extreme weather and Category 2 extreme weather in the region during the past preset period, as well as the Category 1 value and Category 2 value of each grid, calculate the Category 1 probability and Category 2 probability of each grid, where the Category 1 probability is the ratio of the Category 1 value to the number of occurrences of Category 1 extreme weather, and the Category 2 probability is the ratio of the Category 2 value to the number of occurrences of Category 2 extreme weather;

[0020] S2350. If at least one of extreme weather type 1 and extreme weather type 2 is likely to occur in a set time period in the future, then based on the size of the probability of type 1 and type 2 in each grid, predict the probability of power operation tasks caused by extreme weather type 1 and / or extreme weather type 2 in each grid area.

[0021] Furthermore, the step S300 includes the following steps:

[0022] S310: When receiving an operation request from a power operation site in the region, dispatch one or more service cabins to the nearest mobile service cabin in the dispatch base to provide service based on real-time traffic conditions;

[0023] S320, predicting the duration required for the on-site power operation task;

[0024] S330. Extract the next task to be performed in the predicted task pool and determine the grid area to which the task belongs. If the task is a planned operation task, select the nearest service cabin dispatch base in the grid area to which the task belongs for service based on the operation task occurrence time and the future traffic conditions. If the task is a predicted power emergency repair task, calculate whether the mobile service cabin in the service cabin dispatch base closest to the grid where the task is located meets the task requirements. If so, do not dispatch it, and wait for real-time processing after the task occurs. If it is not enough to meet the task requirements, judge the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold. If it is less than the preset threshold, do not dispatch it in advance, and wait for real-time scheduling after the task occurs. If it is greater than or equal to the preset threshold, dispatch the missing mobile service cabin to the service cabin dispatch base closest to the grid on standby based on the time period in which the power emergency repair task is predicted to occur and the specific road condition information of the time period.

[0025] Furthermore, the step S300 further includes the following steps:

[0026] S400: When receiving an emergency task demand from a power operation site in the region that is not in the predicted task pool, a joint dispatch is performed based on the location information of the power operation site and the service module dispatching base, the predicted task pool, and traffic conditions to dispatch one or more mobile service modules from the nearest service module dispatching bases to meet the operation demand.

[0027] Furthermore, the step S400 includes the following steps:

[0028] S410: When an emergency task occurs, calculate the grid where the task is located and calculate one or more service module dispatching bases nearest to the grid in real time;

[0029] S420, assuming that the service cabin dispatching base dispatches a mobile service cabin to the power operation site for this emergency task, the time required under real-time road conditions is t0, and the departure time is T0; the next task in the predicted task pool is originally planned to require the service cabin dispatching base to dispatch a mobile service cabin for service, the occurrence time is T1, and the time required for the service cabin dispatching base to reach the power operation site is t1; if the service cabin dispatching base dispatches a mobile service cabin to serve this emergency task at time T0, it is not enough to meet the next task in the predicted task pool, then the next task in the predicted task pool needs to select its second closest service cabin dispatching base, assuming that the time required for the second closest service cabin dispatching base to reach the power operation site is t2; calculate the future time as If the future duration is negative, it means that the processing of the emergency task will not affect the next task in the predicted task pool, and no emergency scheduling is required; if the future duration is positive, and the next task in the predicted task pool is a planned operation task, then the importance of the task is judged. If the emergency task is more important, the base dispatches a mobile service module from the nearest service module, and the planned operation task dispatches a mobile service module from the second nearest service module; if the planned operation task is more important, then the emergency task is processed this time, avoiding the service module dispatch base that needs to serve the planned operation task; if the future duration is positive, and the next task in the predicted task pool is a planned operation task, then the importance of the task is judged. If the emergency task is more important, the base dispatches a mobile service module from the nearest service module, and the planned operation task dispatches a mobile service module from the second nearest service module; if the planned operation task is more important, then the emergency task is processed this time, avoiding the service module dispatch base that needs to serve the planned operation task; If a task is a predicted power repair task, the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold is determined. If it is less than the preset threshold, no special dispatch is made, and the nearest service module dispatch base responds to the emergency task; if it is greater than the preset threshold, the importance of the task is determined. If the emergency task is more important, the mobile service module is dispatched from the nearest service module dispatch base, and the predicted power repair task is dispatched from the second nearest service module dispatch base; if the predicted power repair task is more important, the emergency task is handled, avoiding the service module dispatch base that needs to serve the predicted power repair task.

[0030] Furthermore, the step of visualizing the regional map is further included between steps S100 and S200, which specifically includes the following steps: in ArcGIS software, a base map layer of the regional range is newly created, and the base map can be a high-precision raster image; a new vector layer is newly created, and all the power operation sites and service cabin dispatching bases in the region are stored as point elements; the newly created vector layer should be consistent with the coordinate system of the base map layer; in the point element representing the service cabin dispatching base, a new attribute is newly created to store the number of mobile service cabins owned by the service cabin dispatching base; a new service cabin dispatching base visualization label is newly created, and the label content includes: how many mobile service cabins the service cabin dispatching base has, how many mobile service cabins there are currently, and how long the dispatched mobile service cabins have been working; the label is visually displayed next to the point element of the service cabin dispatching base in the vector layer; a new power operation site visualization label is newly created, and the label content includes: whether there is power operation at the power operation site, if there is power operation, how many mobile service cabins are operating, and how long the mobile service cabins have been operating; the label is visually displayed next to the point element of the power operation site in the vector layer;

[0031] The step S300 is followed by a step of real-time visualization of the shared mobile service cabin, which specifically includes the following steps: aligning and overlaying the defined base map layer and vector layer in ArcMap for display; acquiring the GPS coordinate information of each mobile service cabin in real time, creating a new service cabin layer in the map, and dynamically displaying it in the form of point features; displaying the service cabin dispatching base in a first color, and visually displaying the current number of mobile service cabins on-site at the service cabin dispatching base in different shades of the first color value, where the darker the first color, the more mobile service cabins on-site; displaying the power operation site in a second color, and visually displaying the operation progress at the power operation site in different shades of the second color value, where the darker the second color, the faster the operation progress; and refreshing all visual display data once every set time period.

[0032] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-compartment collaborative scheduling method for shared service compartments at an electric power operation site as described above.

[0033] The present invention also provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the multi-cabin collaborative scheduling method for shared service cabins at an electric power operation site as described above are implemented.

[0034] The present invention provides a method, medium, and device for collaborative scheduling of multiple shared service cabins at a power operation site, which has the following beneficial effects:

[0035] The present invention obtains basic information of all power operation sites and service cabin dispatching bases in the area, combines historical operation task information and weather forecast data, predicts predicted power emergency repair tasks that may occur in the future set time period in the area, and constructs a predicted task pool by comprehensively predicting power emergency repair tasks and planned power operation tasks. When receiving operation requirements from power operation sites in the area, the present invention jointly dispatches mobile service cabins according to the location information of the power operation site and the service cabin dispatching base, the predicted task pool and traffic conditions, and ensures that when the operation requirement is a predicted power emergency repair task, the service cabin dispatching base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation requirement; it can predict that sudden power failures due to weather reasons may occur in the future based on historical operation task information and weather forecast data, and thus dispatch multiple shared mobile service cabins to ensure that the service cabin dispatching base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation requirement, which can shorten the time required for emergency repairs and realize the timely restoration of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1This is a flow chart of a method for collaboratively dispatching multiple shared service cabins at a power operation site provided by the present invention;

[0037] Figure 2 It is a structural block diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the accompanying drawings provided by the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are all in a very simplified form and are not in exact proportions. They are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0039] Example 1: This example provides a method for coordinating multiple shared service cabins in a power operation site. Figure 1 As shown, the method includes the following steps:

[0040] S100: Obtain basic information of all power operation sites and service module dispatching bases in the region, including their location information and historical operation task information.

[0041] Specifically, the steps include: obtaining the location information of all power operation sites in the area, preferably recording it in the form of longitude and latitude coordinates; obtaining the historical operation task information of all power operation sites in the area in the past preset time period, including the time data of each task and the service cabin data required for each task; obtaining the hardware information of the service cabin dispatching base, including the number of mobile service cabins owned by the service cabin dispatching base and the location information of the service cabin dispatching base, preferably recording it in the form of longitude and latitude coordinates; obtaining the historical service information of the service cabin dispatching base, including the number and type of power operation tasks undertaken by the service cabin dispatching base in the past preset time period, the number of mobile service cabins dispatched for each task, and the mobile service cabin dispatching service time data.

[0042] In a preferred embodiment, the preset period is 1 year.

[0043] S200. Combine historical operation task information and weather forecast data to predict the predicted power emergency repair tasks that may occur in the region within a set period of time in the future, and build a prediction task pool by comprehensively predicting the power emergency repair tasks and planned power operation tasks.

[0044] The specific steps include:

[0045] S210: Construct a power operation history task pool based on the locations of all power operation sites in the region, and the time points and durations of each operation task in the basic information;

[0046] S220. Divide the power operations in the historical task pool into planned operation tasks and emergency repair tasks based on the nature of the power operation tasks. Planned operation tasks are power operation tasks that are scheduled in advance, and emergency repair tasks are emergency repair tasks performed in response to sudden power failures.

[0047] S230, predicting possible power emergency repair tasks based on the emergency repair tasks in the historical task pool and the weather forecast data in the area;

[0048] The specific steps include:

[0049] S2310. Based on weather forecast data for the area during a set period in the future, determine the possibility of extreme weather in the area during the set period in the future. The extreme weather is divided into two categories: Category 1 extreme weather refers to strong winds, heavy rain, or heavy snow that directly damage power supply facilities; Category 2 extreme weather refers to excessively high or low temperatures that indirectly damage power supply facilities due to excessive power load.

[0050] S2320: Acquire weather information for a preset period in the past within the region, further divide the power operation history task pool, and extract power operation history tasks caused by extreme weather type 1 and extreme weather type 2;

[0051] S2330: Divide the area into grids of a set size, and count the historical power operation tasks caused by Category 1 extreme weather and Category 2 extreme weather that occurred in each grid within a preset period of time. If each occurrence occurs once, the grid count is incremented by 1; the Category 1 and Category 2 values ​​of all grids in the area can be obtained.

[0052] S2340, based on the number of occurrences of Category 1 extreme weather and Category 2 extreme weather in the region during the past preset period, as well as the Category 1 value and Category 2 value of each grid, calculate the Category 1 probability and Category 2 probability of each grid, where the Category 1 probability is the ratio of the Category 1 value to the number of occurrences of Category 1 extreme weather, and the Category 2 probability is the ratio of the Category 2 value to the number of occurrences of Category 2 extreme weather;

[0053] S2350. If at least one of extreme weather type 1 and extreme weather type 2 is likely to occur in a set time period in the future, then based on the size of the probability of type 1 and type 2 in each grid, predict the probability of power operation tasks caused by extreme weather type 1 and / or extreme weather type 2 in each grid area.

[0054] S240, combining official data, collecting and extracting planned work tasks for a set period in the future within the region;

[0055] S250, comprehensively plan operation tasks and predict power emergency repair tasks to build a prediction task pool within a future set time period area.

[0056] Among them, the planned operation task is 100% likely to occur, and the predicted power emergency repair task is predicted to have a certain probability of occurrence based on historical probability. The predicted occurrence time of the predicted power emergency repair task should be accurate to the hour.

[0057] In a preferred embodiment, the set time period is 1 day.

[0058] In a preferred embodiment, in step S2330 , the area is divided into grids of 1 km×1 km in size.

[0059] S300. When receiving an operation demand from a power operation site in the region, the mobile service cabin is jointly dispatched according to the location information of the power operation site and the service cabin dispatch base, the predicted task pool, and the traffic conditions, and it is ensured that when the operation demand is a predicted power repair task, the service cabin dispatch base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation demand.

[0060] The specific steps include:

[0061] S310: When receiving an operation request from a power operation site in the region, dispatch one or more service cabins to the nearest mobile service cabin in the dispatch base to provide service based on real-time traffic conditions;

[0062] S320: Predicting the duration required for the power operation site task; preferably, the required duration is obtained by manual prediction based on the assigned task, or by averaging the duration of historical tasks at the power operation site;

[0063] S330. Extract the next task to be performed in the predicted task pool and determine the grid area to which the task belongs. If the task is a planned operation task, select the nearest service cabin dispatch base in the grid area to which the task belongs for service based on the operation task occurrence time and the future traffic conditions. If the task is a predicted power emergency repair task, calculate whether the mobile service cabin in the service cabin dispatch base closest to the grid where the task is located meets the task requirements. If so, do not dispatch it, and wait for real-time processing after the task occurs. If it is not enough to meet the task requirements, judge the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold. If it is less than the preset threshold, do not dispatch it in advance, and wait for real-time scheduling after the task occurs. If it is greater than or equal to the preset threshold, dispatch the missing mobile service cabin to the service cabin dispatch base closest to the grid on standby based on the time period in which the power emergency repair task is predicted to occur and the specific road condition information of the time period.

[0064] In a preferred embodiment, a new layer is created in ArcGIS, and real-time traffic network information or predicted data of future traffic conditions is imported through the Baidu Map API. The time required for each service module dispatch base to reach the power operation site under real-time traffic conditions or future traffic conditions is calculated, and the distance between the service module dispatch base and the power operation site is determined based on the required time.

[0065] In a preferred embodiment, if the nearest service module dispatching base has a mobile service module for outbound operations, the return time of the mobile service module for outbound operations needs to be predicted. If the mobile service module can return before the time period when the next mission will occur, the returned mobile service module can be counted as the next mobile service module that can be dispatched for the next mission.

[0066] In a preferred embodiment, at the nearest service module dispatching base, if there is a standby mobile service module dispatched from another service module dispatching base, and the mobile service module for outbound operations that is not in the plan returns early, the dispatched standby mobile service module will be sent back to the original service module dispatching base.

[0067] The embodiment of the present invention obtains basic information of all power operation sites and service cabin dispatching bases in a region, combines historical operation task information and weather forecast data, predicts predicted power emergency repair tasks that may occur in the region within a set time period in the future, and constructs a predicted task pool by comprehensively predicting power emergency repair tasks and planned power operation tasks. When an operation demand is received from a power operation site in the region, the mobile service cabin is jointly dispatched according to the location information of the power operation site and the service cabin dispatching base, the predicted task pool and traffic conditions, and ensures that when the operation demand is a predicted power emergency repair task, the service cabin dispatching base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation demand; based on historical operation task information and weather forecast data, it can be predicted that sudden power failures due to weather reasons may occur in the future, so that multiple shared mobile service cabins are dispatched to ensure that the service cabin dispatching base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation demand, which can shorten the time required for emergency repair and realize the timely restoration of power supply.

[0068] In an optional embodiment, the method for collaboratively scheduling multiple shared service cabins at a power operation site further includes the following steps after step S300:

[0069] S400: When receiving an emergency task demand from a power operation site in the region that is not in the predicted task pool, a joint dispatch is performed based on the location information of the power operation site and the service module dispatching base, the predicted task pool, and traffic conditions to dispatch one or more mobile service modules from the nearest service module dispatching bases to meet the operation demand.

[0070] The specific steps include:

[0071] S410: When an emergency task occurs, calculate the grid where the task is located and calculate one or more service module dispatching bases nearest to the grid in real time;

[0072] S420, assuming that the service cabin dispatching base dispatches a mobile service cabin to the power operation site for this emergency task, the time required under real-time road conditions is t0, and the departure time is T0; the next task in the predicted task pool is originally planned to require the service cabin dispatching base to dispatch a mobile service cabin for service, the occurrence time is T1, and the time required for the service cabin dispatching base to reach the power operation site is t1; if the service cabin dispatching base dispatches a mobile service cabin to serve this emergency task at time T0, it is not enough to meet the next task in the predicted task pool, then the next task in the predicted task pool needs to select its second closest service cabin dispatching base, assuming that the time required for the second closest service cabin dispatching base to reach the power operation site is t2; calculate the future time as If the future duration is negative, it means that the processing of the emergency task will not affect the next task in the predicted task pool, and no emergency scheduling is required; if the future duration is positive, and the next task in the predicted task pool is a planned operation task, then the importance of the task is judged. If the emergency task is more important, the base dispatches a mobile service module from the nearest service module, and the planned operation task dispatches a mobile service module from the second nearest service module; if the planned operation task is more important, then the emergency task is processed this time, avoiding the service module dispatch base that needs to serve the planned operation task; if the future duration is positive, and the next task in the predicted task pool is a planned operation task, then the importance of the task is judged. If the emergency task is more important, the base dispatches a mobile service module from the nearest service module, and the planned operation task dispatches a mobile service module from the second nearest service module; if the planned operation task is more important, then the emergency task is processed this time, avoiding the service module dispatch base that needs to serve the planned operation task; If a task is a predicted power repair task, the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold is determined. If it is less than the preset threshold, no special dispatch is made, and the nearest service module dispatch base responds to the emergency task; if it is greater than the preset threshold, the importance of the task is determined. If the emergency task is more important, the mobile service module is dispatched from the nearest service module dispatch base, and the predicted power repair task is dispatched from the second nearest service module dispatch base; if the predicted power repair task is more important, the emergency task is handled, avoiding the service module dispatch base that needs to serve the predicted power repair task.

[0073] Among them, the importance of the task can be judged by submitting it to the administrator for manual judgment and receiving the judgment result, or it can be automatically judged by setting judgment conditions, such as judging according to the type of task operation, the location of the power operation site, etc.

[0074] In an optional embodiment, the method for collaboratively dispatching multiple shared service cabins at a power operation site further includes a step of visualizing a regional map between steps S100 and S200. By constructing a map system, a map visualization of the power operation site and the service cabin dispatching base based on the latest regional map is achieved. Specifically, the steps include:

[0075] In ArcGIS software, create a base map layer for the area. The base map can be a high-precision raster image.

[0076] Create a new vector layer to store all power operation sites and service cabin dispatch bases in the area with point features; the newly created vector layer should be consistent with the coordinate system of the base map layer; in the point feature representing the service cabin dispatch base, create a new attribute to store the number of mobile service cabins owned by the service cabin dispatch base; create a new service cabin dispatch base visualization label, the label content includes: how many mobile service cabins the service cabin dispatch base has, how many mobile service cabins there are currently, and how long the dispatched mobile service cabins have been working; the label is visually displayed next to the point feature of the service cabin dispatch base in the vector layer; create a new power operation site visualization label, the label content includes: whether there is power operation at the power operation site, if there is power operation, how many mobile service cabins are operating, and how long the mobile service cabins have been operating; the label is visually displayed next to the point feature of the power operation site in the vector layer.

[0077] In an optional embodiment, the method for collaboratively scheduling multiple shared service cabins at a power operation site further includes, after step S300, a step of real-time visualization of shared mobile service cabins, for visually displaying information of all mobile service cabins on a map in real time, specifically comprising the following steps:

[0078] Align and overlay the defined base map layer and vector layer in ArcMap;

[0079] The GPS coordinates of each mobile service module are acquired in real time, and a new service module layer is created on the map, which is dynamically displayed as a point feature. The service module dispatch base is displayed in the first color, and the number of mobile service modules currently on-site at the service module dispatch base is visualized using different shades of the first color. The darker the first color, the more mobile service modules are on-site. The power operation site is displayed in the second color, and the operation progress at the power operation site is visualized using different shades of the second color. The darker the second color, the faster the operation progress.

[0080] All visual display data is refreshed every set time.

[0081] In a preferred embodiment, the first color is green, the second color is red, and the set duration is 10 seconds.

[0082] Embodiment 2: This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the multi-cabin collaborative scheduling method for shared service cabins at a power operation site described above are implemented.

[0083] The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium may also include a combination of the above types of memory.

[0084] Example 3: This embodiment provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the multi-cabin collaborative scheduling method for shared service cabins at a power operation site described above are implemented.

[0085] like Figure 2 As shown, the computer device may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, a memory 74, and at least one communication bus 72. The communication bus 72 is used to realize the connection and communication between these components. The communication interface 73 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 73 may also include a standard wired interface and a wireless interface. The memory 74 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 74 may optionally be at least one storage device located away from the aforementioned processor 71. The memory 74 stores application programs, and the processor 71 calls the program code stored in the memory 74 to execute any of the above method steps.

[0086] The communication bus 72 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The communication bus 72 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0087] Among them, the memory 74 may include a volatile memory (English: volatile memory), such as a random-access memory (English: random-access memory, abbreviated: RAM); the memory may also include a non-volatile memory (English: non-volatile memory), such as a flash memory (English: flash memory), a hard disk drive (English: hard disk drive, abbreviated: HDD) or a solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 74 may also include a combination of the above types of memory.

[0088] The processor 71 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and a NP.

[0089] The processor 71 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0090] Optionally, the memory 74 is further configured to store program instructions. The processor 71 may call the program instructions to implement the method for collaborative scheduling of multiple shared service cabins at a power operation site according to the present invention.

[0091] Those skilled in the art should understand that the present invention can be implemented in many other specific forms without departing from the spirit and scope of the present invention. Based on the embodiments of the present invention, any changes and modifications made by ordinary technicians in the field of the present invention in accordance with the above disclosure are within the scope of protection of the claims.

Claims

1. A method for collaborative scheduling of multiple shared service cabins at a power operation site, characterized in that: The method comprises the following steps: S100, obtaining basic information of all power operation sites and service module dispatch bases in the region, including their location information and historical operation task information; S200, combining historical operation task information and weather forecast data, predicting the predicted power emergency repair tasks that may occur in the future set time period in the region, and constructing a predicted task pool by comprehensively predicting the power emergency repair tasks and planned power operation tasks; the step S200 includes: S230, predicting the predicted power emergency repair tasks that may occur based on the emergency repair tasks in the historical task pool and the weather forecast data in the region; the step S230 includes the following steps: S2310, based on the weather forecast data of the region in the future set time period, obtaining the possibility of extreme weather in the region in the future set time period, and dividing extreme weather into two categories: extreme weather category 1 refers to strong winds, heavy rains, and heavy snows that cause direct damage to power supply facilities; extreme weather category 2 refers to excessively high or low temperatures that cause indirect damage to power facilities due to excessive power load; S23 20. Obtain weather information for a preset period of time in the past within the region, further divide the power operation history task pool, and extract the power operation history tasks caused by extreme weather type 1 and extreme weather type 2; S2330. Divide the region into grids of a set size, and count the power operation history tasks caused by extreme weather type 1 and extreme weather type 2 that occurred in the preset period of time in each grid. If the task occurs once, the grid count is accumulated by 1; the Class 1 value and Class 2 value of all grids in the region can be obtained; S2340. Based on the number of times extreme weather type 1 and extreme weather type 2 occurred in the region during the preset period of time, as well as the Class 1 value and Class 2 value of each grid, calculate the Class 1 probability and Class 2 probability of each grid, where the Class 1 probability is the ratio of the Class 1 value to the number of occurrences of extreme weather type 1, and the Class 2 probability is the ratio of the Class 2 value to the number of occurrences of extreme weather type 2; S300, when receiving the operation demand from the power operation site in the area, the mobile service cabin is jointly dispatched according to the location information of the power operation site and the service cabin dispatch base, the predicted task pool and the traffic conditions, and it is ensured that when the operation demand is a predicted power repair task, the service cabin dispatch base closest to the power operation site has a sufficient number of mobile service cabins to meet the operation demand; the step S300 includes: S330, extracting the next task to be performed in the predicted task pool, and determining the grid area to which the task belongs; if the task is a planned operation task, then according to the occurrence time of the operation task and the future traffic conditions, the service cabin dispatch base closest to the grid area to which the task belongs is selected; The service cabin is dispatched to the base for service; if the task is a predicted power emergency repair task, it is calculated whether the mobile service cabin in the service cabin dispatch base closest to the grid where the task is located meets the task requirements. If so, it will not be dispatched and will be processed in real time after the task occurs; if it is not enough to meet the task requirements, the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold is judged. If it is less than the preset threshold, it will not be dispatched in advance and will be dispatched in real time after the task occurs. If it is greater than or equal to the preset threshold, the missing mobile service cabin will be dispatched to the service cabin dispatch base closest to the grid on standby based on the time period when the power emergency repair task is predicted to occur and the specific road condition information of the time period.

2. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 1, characterized in that: Step S100 includes the following steps: obtaining location information of all power operation sites in the area; obtaining historical operation task information of all power operation sites in the area within a preset period of time in the past, including time data of each task and service module data required for each task; obtaining hardware information of the service module dispatching base, including the number of mobile service modules owned by the service module dispatching base and the location information of the service module dispatching base; Obtain historical service information of the service module dispatch base, including the number and type of power operation tasks undertaken by the service module dispatch base in the past preset period, the number of mobile service modules dispatched for each task, and the mobile service module dispatch service time data.

3. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 2, characterized in that: The step S200 includes the following steps: S210: Construct a power operation history task pool based on the locations of all power operation sites in the region, and the time points and durations of each operation task in the basic information; S220. Divide the power operations in the historical task pool into planned operation tasks and emergency repair tasks based on the nature of the power operation tasks. Planned operation tasks are power operation tasks that are scheduled in advance, and emergency repair tasks are emergency repair tasks performed in response to sudden power failures. S230, predicting possible power emergency repair tasks based on the emergency repair tasks in the historical task pool and the weather forecast data in the area; S240, combining official data, collecting and extracting planned work tasks for a set period in the future within the region; S250, comprehensively plan operation tasks and predict power emergency repair tasks to build a prediction task pool within a future set time period area.

4. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 3 is characterized in that: The step S230 further includes the following steps: S2350. If at least one of extreme weather type 1 and extreme weather type 2 is likely to occur in a set time period in the future, then based on the size of the probability of type 1 and type 2 in each grid, predict the probability of power operation tasks caused by extreme weather type 1 and / or extreme weather type 2 in each grid area.

5. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 4, characterized in that: The step S300 includes the following steps: S310: When receiving an operation request from a power operation site in the region, dispatch one or more service cabins to the nearest mobile service cabin in the dispatch base to provide service based on real-time traffic conditions; S320, predicting the duration required for the on-site power operation task; S330, extracting the next task to be performed in the prediction task pool, and determining the grid area to which the task belongs; If the task is a planned operation task, the service module dispatch base closest to the grid area where the task belongs will be selected to provide service based on the task occurrence time and future traffic conditions; If the task is a predicted power emergency repair task, calculate whether the mobile service cabin in the service cabin dispatch base closest to the grid where the task is located meets the task requirements. If so, it will not be dispatched and will be processed in real time after the task occurs. If it is not enough to meet the task requirements, judge the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold. If it is less than the preset threshold, it will not be dispatched in advance and will be dispatched in real time after the task occurs. If it is greater than or equal to the preset threshold, the missing mobile service cabin will be dispatched to the service cabin dispatch base closest to the grid on standby based on the time period when the power emergency repair task is predicted to occur and the specific road conditions information during that time period.

6. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 5, characterized in that: The step S300 further includes the following steps: S400: When receiving an emergency task demand from a power operation site in the region that is not in the predicted task pool, a joint dispatch is performed based on the location information of the power operation site and the service module dispatching base, the predicted task pool, and traffic conditions to dispatch one or more mobile service modules from the nearest service module dispatching bases to meet the operation demand.

7. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 6, characterized in that: The step S400 includes the following steps: S410: When an emergency task occurs, calculate the grid where the task is located and calculate one or more service module dispatching bases nearest to the grid in real time; S420, assuming that the service cabin dispatching base dispatches a mobile service cabin to the power operation site for this emergency task, the time required under real-time road conditions is t0, and the departure time is T0; the next task in the predicted task pool is originally planned to require the service cabin dispatching base to dispatch a mobile service cabin for service, the occurrence time is T1, and the time required for the service cabin dispatching base to reach the power operation site is t1; if the service cabin dispatching base dispatches a mobile service cabin to serve this emergency task at time T0, it is not enough to meet the next task in the predicted task pool, then the next task in the predicted task pool needs to select its second closest service cabin dispatching base, assuming that the time required for the second closest service cabin dispatching base to reach the power operation site is t2; calculate the future time as If the future duration is negative, it means that the processing of the emergency task will not affect the next task in the predicted task pool, and no emergency scheduling is required; if the future duration is positive, and the next task in the predicted task pool is a planned operation task, then the importance of the task is judged. If the emergency task is more important, the base dispatches a mobile service module from the nearest service module, and the planned operation task dispatches a mobile service module from the second nearest service module; if the planned operation task is more important, then the emergency task is processed this time, avoiding the service module dispatch base that needs to serve the planned operation task; if the future duration is positive, and the next task in the predicted task pool is a planned operation task, then the importance of the task is judged. If the emergency task is more important, the base dispatches a mobile service module from the nearest service module, and the planned operation task dispatches a mobile service module from the second nearest service module; if the planned operation task is more important, then the emergency task is processed this time, avoiding the service module dispatch base that needs to serve the planned operation task; If a task is a predicted power repair task, the size of the Class 1 probability and / or Class 2 probability of the grid and the preset threshold is determined. If it is less than the preset threshold, no special dispatch is made, and the nearest service module dispatch base responds to the emergency task; if it is greater than the preset threshold, the importance of the task is determined. If the emergency task is more important, the mobile service module is dispatched from the nearest service module dispatch base, and the predicted power repair task is dispatched from the second nearest service module dispatch base; if the predicted power repair task is more important, the emergency task is handled, avoiding the service module dispatch base that needs to serve the predicted power repair task.

8. The method for collaborative dispatching of multiple shared service cabins at a power operation site according to claim 1, characterized in that: The step between steps S100 and S200 also includes a step of visualizing the regional map, which specifically includes the following steps: in ArcGIS software, creating a base map layer for the regional scope, where the base map is a high-precision raster image; creating a new vector layer, storing all power operation sites and service module dispatching bases in the region as point elements; the new vector layer should be consistent with the coordinate system of the base map layer; creating a new attribute in the point element representing the service module dispatching base to store the number of mobile service modules owned by the service module dispatching base; A new visualization label for the service module dispatch base is created. The label content includes: the total number of mobile service modules at the service module dispatch base, the current number of mobile service modules, and the operating time of the dispatched mobile service modules; the label is visually displayed next to the point feature of the service module dispatch base in the vector layer; a new visualization label for the power operation site is created. The label content includes: whether there is power operation at the power operation site, if there is power operation, the total number of mobile service modules in operation, and the operating time of the mobile service modules; the label is visually displayed next to the point feature of the power operation site in the vector layer; The step S300 is followed by a step of real-time visualization of the shared mobile service cabin, which specifically includes the following steps: aligning and overlaying the defined base map layer and vector layer in ArcMap for display; acquiring the GPS coordinate information of each mobile service cabin in real time, creating a new service cabin layer in the map, and dynamically displaying it in the form of point features; displaying the service cabin dispatching base in a first color, and visually displaying the current number of mobile service cabins on-site at the service cabin dispatching base in different shades of the first color value, where the darker the first color, the more mobile service cabins on-site; displaying the power operation site in a second color, and visually displaying the operation progress at the power operation site in different shades of the second color value, where the darker the second color, the faster the operation progress; and refreshing all visual display data once every set time period.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for collaborative scheduling of multiple shared service cabins at an electric power operation site as described in any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the multi-cabin collaborative scheduling method for shared service cabins at an electric power operation site according to any one of claims 1 to 8 are implemented.

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