A resource command control optimization method and system

Abstract

The application provides a resource command control optimization method and system, and relates to the field of resource control.The method comprises the following steps: obtaining initial task information and task execution team information; setting a resource consumption type; obtaining real-time environment information and real-time execution conditions of each execution team; determining first predicted consumption and second predicted consumption of the first type resource and the second type resource according to the real-time environment information and the real-time execution conditions; setting first dynamic weight and second dynamic weight according to the real-time execution conditions, the initial task information, the task execution team information, the first predicted consumption and the second predicted consumption; and determining a control optimization scheme according to the initial task information, the real-time environment information, the first dynamic weight and the second dynamic weight.According to the application, resources can be supplied to each execution team under the condition that the influence on the task is minimized, and the convenience of each execution team in executing the task is improved.

Description

Technical Field

[0001] This invention relates to the field of resource control, and more particularly to a resource command and control optimization method and system. Background Technology

[0002] In related technologies, resources can be commanded and controlled by professionals in conjunction with the real-time execution status of tasks. In other words, it mainly relies on human factors, and the workload of command work is enormous. Therefore, over-reliance on human factors may make it difficult to maintain the accuracy of command and control plans.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a resource command and control optimization method and system, which can solve the technical problem that related technologies have difficulty in maintaining the accuracy of command and control schemes.

[0005] According to a first aspect of the present invention, a resource command and control optimization method is provided, comprising:

[0006] Obtain initial task information and task execution team information;

[0007] Define resource consumption types, wherein the resource consumption types include: a first type of resource and a second type of resource;

[0008] At multiple points in the control cycle, acquire real-time environmental information and real-time execution status of each execution team;

[0009] Based on the real-time environmental information and the real-time execution status, determine the first and second predicted consumption amounts of the first type of resources and the second type of resources;

[0010] Based on the real-time execution status, the initial task information, the task execution team information, the first predicted consumption, and the second predicted consumption, a first dynamic weight and a second dynamic weight are set.

[0011] Based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight, a control optimization scheme is determined.

[0012] According to the present invention, setting resource consumption types includes:

[0013] Define a first type of resource, wherein the consumption of the first type of resource is strongly correlated with the task time;

[0014] A second type of resource is defined, wherein the consumption of the second type of resource is strongly correlated with unexpected situations in the task.

[0015] According to the present invention, determining the first predicted consumption and the second predicted consumption of the first type of resource and the second type of resource based on the real-time environmental information and the real-time execution status includes:

[0016] Based on the real-time execution status, obtain the amount of first type of resources consumed, task execution progress, amount of second type of resources consumed, and number of sudden events.

[0017] Based on the real-time environment information, determine the difficulty coefficient of the real-time task environment;

[0018] Determine the task execution efficiency based on the task execution progress;

[0019] The consumption rate of the first type of resources is determined based on the amount of the first type of resources consumed and the task execution progress.

[0020] The first predicted consumption and the second predicted consumption are determined based on the consumption amount of the first type of resources, the consumption rate of the first type of resources, the task execution progress, the consumption amount of the second type of resources, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency.

[0021] According to the present invention, determining the real-time task environment difficulty coefficient based on the real-time environment information includes:

[0022] Based on the real-time environmental information, determine the real-time terrain, real-time temperature, and real-time weather;

[0023] Based on the real-time terrain, determine the real-time terrain difficulty coefficient;

[0024] Based on the real-time temperature, determine the real-time temperature difficulty coefficient;

[0025] Based on the real-time weather, determine the real-time weather difficulty coefficient;

[0026] The real-time task environment difficulty coefficient is determined based on the real-time terrain difficulty coefficient, the real-time temperature difficulty coefficient, and the real-time weather difficulty coefficient.

[0027] According to the present invention, determining the first predicted consumption and the second predicted consumption based on the consumed amount of the first type of resource, the consumption rate of the first type of resource, the task execution progress, the consumed amount of the second type of resource, the number of occurrences of the sudden event, the difficulty coefficient of the real-time task environment, and the task execution efficiency includes: according to the formula:

[0028]

[0029] Determine the first control cycle The first predicted consumption of the first type of resource at time e. and the first control cycle Second predicted consumption of the second type of resource at time r ,in, To predict the time point, To determine the amount of the first type of resource consumed at time t of the control period. To control the task execution progress at time t of the control cycle To control the consumption rate of the first type of resource at time t of the control period, To determine the real-time task environment difficulty coefficient at time t of the control cycle. To control the task execution efficiency at time t of the control cycle. This refers to the amount of second-type resource consumed by the e-th first-type resource at time t of the control period. This is the number of sudden events that occur at time t of the control period.

[0030] According to the present invention, based on the real-time execution status, the task initial information, the task execution team information, the first predicted consumption, and the second predicted consumption, a first dynamic weight and a second dynamic weight are set, including:

[0031] Based on the initial task information, determine the first preset weight and the second preset weight;

[0032] Based on the task execution group information, determine the number of times the first type of group successfully executed tasks and the number of times the second type of group successfully executed tasks;

[0033] Based on the real-time execution status, determine the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type of resource, and the remaining amount of the second type of resource;

[0034] Based on the first preset weight, the second preset weight, the number of times the first type of group successfully executed tasks, the number of times the second type of group successfully executed tasks, the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type of resource, and the remaining amount of the second type of resource, a first dynamic weight and a second dynamic weight are set.

[0035] According to the present invention, a first dynamic weight and a second dynamic weight are set based on the first preset weight, the second preset weight, the number of times the first type of group successfully executes tasks, the number of times the second type of group successfully executes tasks, the first resource-task correlation coefficient, the second resource-task correlation coefficient, the remaining amount of the first type of resource, and the remaining amount of the second type of resource, including: according to the formula:

[0036]

[0037] Determine the first dynamic weight of the e-th type of resource at time t of the control period. and the second dynamic weight of the r-th second type of resource ,in, The first preset weight of the e-th first type resource The second preset weight of the r-th second type resource The first resource task correlation coefficient is the first type of resource at time t of the control period. The second resource task correlation coefficient is the second type resource at time t of the control period. The number of times the first type of group successfully executed the task. The number of times the second type of group successfully performed the task. To preset the number of successful attempts, The remaining amount of the first type of resource at time t of the control period is the amount of the first type of resource remaining. The remaining amount of the second type of resource at time t of the control period is the amount of the second type of resource remaining. For the first control cycle The first predicted consumption of the e-th type of resource at time e. For the first control cycle The second predicted consumption of the second type of resource at time r.

[0038] According to the present invention, a control optimization scheme is determined based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight, including:

[0039] Based on the real-time environmental information, determine the real-time scheduling distance and real-time location information;

[0040] Based on the initial task information, determine the task type;

[0041] The scheduling difficulty coefficient is determined based on the real-time location information and the task type;

[0042] The priority scheduling coefficient is determined based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight.

[0043] An optimized control scheme is determined based on the priority scheduling coefficient and the task type.

[0044] According to the present invention, determining the priority scheduling coefficient based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight includes: according to the formula:

[0045]

[0046] Determine the priority scheduling coefficient of the k-th execution group at time t of the control cycle. Where max is the function for finding the maximum value. Let e ​​be the first dynamic weight of the first type of resource in the k-th execution group at time t of the control cycle. Let r be the second dynamic weight of the r-th type of resource in the k-th execution group at time t of the control cycle. Let be the scheduling difficulty coefficient of the k-th execution group at time t of the control cycle. Let K be the real-time scheduling distance of the k-th execution group at time t of the control cycle, where K is the number of execution groups, k≤K, E is the number of first-type resources, e≤E, R is the number of second-type resources, r≤R, and e, E, r, R, k, and K are all positive integers.

[0047] According to a second aspect of the present invention, a resource command and control optimization system is provided, comprising:

[0048] The initial information module is used to obtain initial task information and task execution team information;

[0049] The type setting module is used to set the resource consumption type, wherein the resource consumption type includes: a first type of resource and a second type of resource;

[0050] The real-time information module is used to acquire real-time environmental information and real-time execution status of each execution team at multiple points in the control cycle.

[0051] The consumption prediction module is used to determine the first predicted consumption and the second predicted consumption of the first type of resources and the second type of resources based on the real-time environmental information and the real-time execution status.

[0052] The weight setting module is used to set a first dynamic weight and a second dynamic weight based on the real-time execution status, the task initial information, the task execution team information, the first predicted consumption, and the second predicted consumption.

[0053] The scheme determination module is used to determine a control optimization scheme based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight.

[0054] According to the present invention, based on the real-time environmental information and real-time execution status of each execution team, the first and second predicted consumption amounts of the first and second types of resources carried by each execution team can be predicted. Based on the real-time execution status, initial task information, task execution team information, first and second predicted consumption amounts, the real-time importance of the first and second types of resources can be evaluated, and a first and second dynamic weight can be set. Furthermore, based on the initial task information, real-time environmental information, first and second dynamic weights, a control optimization scheme can be determined, thereby replenishing resources for each execution team while minimizing the impact on the task, and improving the convenience of each execution team when performing the task. When determining the first and second predicted consumption amounts, the amounts can be determined based on the consumed amount of the first type of resources, the consumption rate of the first type of resources, the task execution rate, the task execution progress, the consumed amount of the second type of resources, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency. During the calculation process, the impact of the predicted remaining task time, the baseline consumption rate, the difficulty coefficient of the real-time task environment, and the task execution efficiency on the first predicted consumption amount, as well as the impact of the predicted number of sudden events and the consumption amount of a single sudden event on the second type of resources on the second predicted consumption amount, can be accurately analyzed. Based on the above impacts and the consumed amount of the first and second type of resources, the first and second predicted consumption amounts are determined, thus improving the accuracy of determining the first and second predicted consumption amounts. When setting the first and second dynamic weights, the first and second dynamic weights can be determined based on the first preset weight, the second preset weight, the number of times the first type of group successfully executed tasks, the number of times the second type of group successfully executed tasks, the task correlation coefficient of the first resource, the task correlation coefficient of the second resource, the remaining amount of the first type of resource, and the remaining amount of the second type of resource. During the calculation process, the first and second dynamic weights can be set based on four aspects: the inherent weight of resources to task types, the correlation between resources and task stages, the experience level of the execution group, and the urgency of resource shortages. This improves the comprehensiveness and accuracy of the first and second dynamic weights. When determining the priority scheduling coefficient, the priority scheduling coefficient can be determined based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight. During the calculation process, the priority scheduling coefficient can be determined based on the relative importance of the most important resource for the k-th execution group, the scheduling difficulty coefficient, and the resources spent on resource scheduling. This improves the accuracy of the priority scheduling coefficient.

[0055] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0057] Figure 1 A schematic flowchart of a resource command and control optimization method according to an embodiment of the present invention is shown as an example;

[0058] Figure 2 A schematic diagram illustrating the setting of resource consumption types according to an embodiment of the present invention is shown;

[0059] Figure 3 An exemplary schematic diagram illustrating the determination of a first predicted consumption and a second predicted consumption according to an embodiment of the present invention is shown.

[0060] Figure 4 An exemplary schematic diagram illustrating the setting of a first dynamic weight and a second dynamic weight according to an embodiment of the present invention is shown;

[0061] Figure 5 An exemplary schematic diagram illustrating the determination of a control optimization scheme according to an embodiment of the present invention is shown;

[0062] Figure 6 A block diagram of a resource command and control optimization system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0064] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0065] Figure 1 A flowchart of a resource command and control optimization method according to an embodiment of the present invention is illustrated, the method comprising:

[0066] Step S1: Obtain initial task information and task execution team information;

[0067] Step S2: Set the resource consumption type, wherein the resource consumption type includes: a first type of resource and a second type of resource;

[0068] Step S3: At multiple points in the control cycle, acquire real-time environmental information and real-time execution status of each execution team;

[0069] Step S4: Based on the real-time environmental information and the real-time execution status, determine the first predicted consumption and the second predicted consumption of the first type of resources and the second type of resources;

[0070] Step S5: Based on the real-time execution status, the task initial information, the task execution team information, the first predicted consumption, and the second predicted consumption, set the first dynamic weight and the second dynamic weight.

[0071] Step S6: Determine the control optimization scheme based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight.

[0072] According to an embodiment of the present invention, the resource command and control optimization method can predict the first and second predicted consumption amounts of the first and second types of resources carried by each execution group based on the real-time environmental information and real-time execution status of each execution group. Based on the real-time execution status, initial task information, task execution group information, first and second predicted consumption amounts, the real-time importance of the first and second types of resources is evaluated, and a first and second dynamic weights are set. Furthermore, based on the initial task information, real-time environmental information, first and second dynamic weights, a control optimization scheme is determined, thereby replenishing resources for each execution group while minimizing the impact on the task, and improving the convenience of each execution group when performing the task.

[0073] According to one embodiment of the present invention, in step S1, task initial information and task execution team information are obtained.

[0074] For example, initial task information (e.g., task type) can be obtained through a pre-specified task plan, and task execution group information (e.g., data on past task execution by each group) can be obtained.

[0075] According to an embodiment of the present invention, in step S2, a resource consumption type is set, wherein the resource consumption type includes: a first type of resource and a second type of resource.

[0076] Figure 2 A schematic diagram illustrating the setting of resource consumption types according to an embodiment of the present invention is shown.

[0077] According to an embodiment of the present invention, step S2 includes:

[0078] Step S21: Set a first type of resource, wherein the consumption of the first type of resource is strongly correlated with the task time;

[0079] Step S22: Set a second type of resource, wherein the consumption of the second type of resource is strongly correlated with unexpected situations in the task.

[0080] For example, the consumption of the first type of resources is strongly correlated with the task time, such as battery power and drinking water. The consumption of the second type of resources is strongly correlated with the occurrence of unexpected situations in the task, and the consumption is sudden and discrete, such as medical supplies and training equipment (when training equipment malfunctions).

[0081] According to one embodiment of the present invention, in step S3, real-time environmental information and real-time execution status of each execution group are acquired at multiple moments in the control cycle.

[0082] For example, real-time environmental information and real-time execution status (e.g., task progress) of each execution team can be obtained through real-time reports from the execution team and sensor devices carried by the training team.

[0083] According to an embodiment of the present invention, in step S4, the first predicted consumption and the second predicted consumption of the first type of resource and the second type of resource are determined based on the real-time environmental information and the real-time execution status.

[0084] Figure 3 A schematic diagram illustrating the determination of a first predicted consumption amount and a second predicted consumption amount according to an embodiment of the present invention is shown.

[0085] According to an embodiment of the present invention, step S4 includes:

[0086] Step S41: Based on the real-time execution status, obtain the amount of first type of resources consumed, task execution progress, amount of second type of resources consumed, and number of sudden events.

[0087] Step S42: Determine the real-time task environment difficulty coefficient based on the real-time environment information;

[0088] Step S43: Determine the task execution efficiency based on the task execution progress;

[0089] Step S44: Determine the consumption rate of the first type of resources based on the amount of the first type of resources consumed and the task execution progress;

[0090] Step S45: Determine the first predicted consumption and the second predicted consumption based on the first type of resource consumption, the first type of resource consumption rate, the task execution progress, the second type of resource consumption, the number of sudden events, the real-time task environment difficulty coefficient, and the task execution efficiency.

[0091] For example, based on the real-time execution status reported by the execution team, the following data is collected: the amount of Type I resources consumed (the percentage of Type I resources consumed relative to the initial amount of Type I resources), the task execution progress (before the task begins, a detailed plan is developed, quantifying the total task workload to 100%. For example, if a patrol route is 10 kilometers long, the task execution progress increases by 10% for every kilometer completed), the amount of Type II resources consumed (the percentage of Type I resources consumed relative to the initial amount of Type I resources), and the number of unexpected events. Based on real-time environmental information, the impact of the environment on the task is assessed, and the real-time task cycle is determined. The difficulty coefficient of the task environment is considered. Based on the task execution progress, the task execution efficiency is determined. For example, before the task starts, a planned progress is set (e.g., the planned progress is 50% one hour after the task starts). The task execution efficiency is determined based on the ratio of the task execution progress to the planned progress. Based on the consumed amount of the first type of resources, the consumption rate of the first type of resources, the task execution rate, the task execution progress, the consumed amount of the second type of resources, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency, the consumption status of the first type of resources and the second type of resources is predicted, and the first predicted consumption amount and the second predicted consumption amount are determined.

[0092] According to an embodiment of the present invention, step S42 includes:

[0093] Step S421: Determine the real-time terrain, real-time temperature, and real-time weather based on the real-time environmental information;

[0094] Step S422: Determine the real-time terrain difficulty coefficient based on the real-time terrain.

[0095] Step S423: Determine the real-time temperature difficulty coefficient based on the real-time temperature.

[0096] Step S424: Determine the real-time weather difficulty coefficient based on the real-time weather.

[0097] Step S425: Determine the real-time task environment difficulty coefficient based on the real-time terrain difficulty coefficient, the real-time temperature difficulty coefficient, and the real-time weather difficulty coefficient.

[0098] For example, based on real-time environmental information, real-time terrain, real-time temperature, and real-time weather are determined; based on real-time terrain, a real-time terrain difficulty coefficient is determined, such as 1 for flat land, 1.3 for hills, and 1.8 for mountains; based on real-time temperature, a real-time temperature difficulty coefficient is determined, such as 1 for temperatures between 15 and 30 degrees Celsius, 1.4 for temperatures above 30 degrees Celsius, 1.1 for temperatures between 0 and 15 degrees Celsius, and 1.4 for temperatures below zero; based on real-time weather, a real-time weather difficulty coefficient is determined, such as 1 for sunny, cloudy, light rain, light snow, heavy rain, and heavy snow, and 1.4 for the same weather conditions; the real-time task environment difficulty coefficient is determined by multiplying the real-time terrain difficulty coefficient, the real-time temperature difficulty coefficient, and the real-time weather difficulty coefficient.

[0099] According to an embodiment of the present invention, step S45 includes: determining the first control cycle according to formula (1). The first predicted consumption of the first type of resource at time e. and the first control cycle Second predicted consumption of the second type of resource at time r ,

[0100] (1)

[0101] in, To predict the time point, To determine the amount of the first type of resource consumed at time t of the control period. To control the task execution progress at time t of the control cycle To control the consumption rate of the first type of resource at time t of the control period, To determine the real-time task environment difficulty coefficient at time t of the control cycle. To control the task execution efficiency at time t of the control cycle. This refers to the amount of second-type resource consumed by the e-th first-type resource at time t of the control period. This is the number of sudden events that occur at time t of the control period.

[0102] According to one embodiment of the present invention, This represents the remaining task progress. , , The ratio of the remaining task progress to the average task execution rate over the previous t time points in the control cycle represents the predicted remaining task time. This indicates the forecast period between the current time and the predicted time. To predict the ratio of the time period to the remaining time, The consumption rate of the first type of resource at time t of the control period for the e-th first type of resource represents the baseline consumption rate used to predict the consumption amount. To control the real-time task environment difficulty coefficient at time t of the cycle, we represent the impact of the task environment conditions on the consumption rate of the first type of resource for the e-th first type of resource. The larger the value, the greater the actual consumption rate of the e-th type-one resource. To control the task execution efficiency at time t of the cycle, higher task execution efficiency means the task is expected to be completed earlier, and a shorter predicted remaining task execution time means a smaller initial prediction cost. This indicates that the control period is determined based on the consumed amount of the first type of resources, the predicted remaining task time, the baseline consumption rate, the real-time task environment difficulty coefficient, and the task execution efficiency. The first predicted consumption at any given time.

[0103] According to one embodiment of the present invention, Let be the ratio of the amount of second-type resource of the first-type resource consumed at time t of the control period to the number of sudden events occurring at time t of the control period, representing the consumption of the second-type resource by a single sudden event. This is the ratio of the number of sudden events occurring at time t of the control period to the task execution progress at time t of the control period, representing the number of sudden events occurring per unit of task execution progress. Indicates the time to the prediction point Continue with the progress of the completed task. This represents the number of predicted sudden events occurring between the t-th time point of the control period and the predicted time point. This indicates that the control period is determined based on the predicted number of sudden events and the consumption of type II resources by a single sudden event. The second predicted consumption of the second type of resource at time r.

[0104] In this way, the first and second predicted consumption amounts can be determined based on the consumption amount of the first type of resources, the consumption rate of the first type of resources, the task execution rate, the task execution progress, the consumption amount of the second type of resources, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency. During the calculation process, the impact of the predicted remaining task time, the baseline consumption rate, the difficulty coefficient of the real-time task environment, and the task execution efficiency on the first predicted consumption amount, as well as the impact of the predicted number of sudden events and the consumption amount of a single sudden event on the second type of resources on the second predicted consumption amount, can be accurately analyzed. Based on the above impacts and the consumption amounts of the first and second type of resources, the first and second predicted consumption amounts are determined, thus improving the accuracy of determining the first and second predicted consumption amounts.

[0105] According to an embodiment of the present invention, in step S5, a first dynamic weight and a second dynamic weight are set based on the real-time execution status, the task initial information, the task execution team information, the first predicted consumption, and the second predicted consumption.

[0106] Figure 4 A schematic diagram illustrating the setting of a first dynamic weight and a second dynamic weight according to an embodiment of the present invention is shown as an example.

[0107] According to an embodiment of the present invention, step S5 includes:

[0108] Step S51: Determine the first preset weight and the second preset weight based on the initial task information;

[0109] Step S52: Based on the task execution group information, determine the number of times the first type of group successfully executed the task and the number of times the second type of group successfully executed the task.

[0110] Step S53: Based on the real-time execution status, determine the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type of resource, and the remaining amount of the second type of resource;

[0111] Step S54: Based on the first preset weight, the second preset weight, the number of times the first type group successfully executed tasks, the number of times the second type group successfully executed tasks, the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type resource, and the remaining amount of the second type resource, set the first dynamic weight and the second dynamic weight.

[0112] For example, firstly, based on the initial mission information, the mission type is determined (e.g., reconnaissance mission and rescue mission). The first and second preset weights are the inherent basic weights of multiple first-type resources and multiple second-type resources for the mission type, respectively, which can be obtained through an expert knowledge base. For instance, for a reconnaissance mission, the first preset weight of batteries is higher; for a rescue mission, the second preset weight of rescue resources is higher. Secondly, based on the mission execution team information, the number of times the first-type team successfully executed missions (the number of times the team successfully executed missions primarily related to first-type resources (e.g., patrol missions)) and the number of times the second-type team successfully executed missions (the number of times the team successfully executed missions primarily related to second-type resources (e.g., rescue missions)) are determined. Finally, based on the real-time execution status, the first-resource mission relevance coefficient (the coefficient of the first-type resource for the current mission stage) is determined. The importance of each resource type is considered. For example, if the current task is in the "physical replenishment phase," then the primary resource (water, food) has a task correlation coefficient of 1. If it has no significant correlation with the task phase, then the corresponding primary resource task correlation coefficient is 0.1. The importance of the secondary resource type to the current task phase is also considered. For example, if the current task is in the "data transmission phase," then the secondary resource task correlation coefficient for the data transmission device is 1. The remaining amount of the primary and secondary resources is also considered. Based on the primary and secondary preset weights, the number of times the primary group successfully executed the task, the number of times the secondary group successfully executed the task, the primary resource task correlation coefficient, the secondary resource task correlation coefficient, the remaining amount of the primary and secondary resources, a primary dynamic weight and a secondary dynamic weight are set.

[0113] According to an embodiment of the present invention, step S54 includes: determining the first dynamic weight of the e-th first type resource at the t-th time of the control period according to formula (2). and the second dynamic weight of the r-th second type of resource ,

[0114] (2)

[0115] in, The first preset weight for the e-th first type resource, The second preset weight is the r-th second type resource. The first resource task correlation coefficient is the first type of resource at time t of the control period. The second resource task correlation coefficient is the second type resource at time t of the control period. The number of times the first type of group successfully executed the task. The number of times the second type of group successfully performed the task. To preset the number of successful attempts, The remaining amount of the first type of resource at time t of the control period is the amount of the first type of resource remaining. The remaining amount of the second type of resource at time t of the control period is the amount of the second type of resource remaining. For the first control cycle The first predicted consumption of the first type of resource at time e. For the first control cycle The second predicted consumption of the second type of resource at time r.

[0116] According to one embodiment of the present invention, This is the ratio of the preset number of successful executions to 1 + the number of successful task executions by the first type of team. The larger this ratio, the more successful task executions the first type of team has, the more experience the team has in performing this type of task, and the more experienced the team is in handling this type of resource shortage. Therefore, the corresponding weight is lower. It can be set to 10. For the first control cycle The value of this value is the ratio of the predicted consumption of the e-th type of resource at time e to the corresponding remaining amount of the e-th type of resource. The larger this ratio is, the greater the predicted consumption of the e-th type of resource, or the smaller the remaining amount of the e-th type of resource, indicating a higher urgency of the shortage of the e-th type of resource. This indicates that the first dynamic weight is determined based on the first preset weight, the first resource task correlation coefficient, the experience of the execution team in performing this type of task, and the urgency of resource shortage.

[0117] According to one embodiment of the present invention, This is the ratio of the preset number of successful executions to 1 + the number of successful task executions by the second type of team. The larger this ratio, the more successful the second type of team is, the more experience the team has in performing this type of task, and the more experienced the team is in handling this type of resource shortage. Therefore, the weight of this ratio is lower. For the first control cycle The second predicted consumption of the second type of resource at time r is minus the ratio of the remaining amount of the second type of resource at time t of the control period to the remaining amount of the second type of resource. The larger this ratio, the greater the first predicted consumption of the second type of resource at time r, or the smaller the remaining amount of the second type of resource at time r, and the higher the urgency of the shortage of the second type of resource at time r. This indicates that the second dynamic weight is determined based on the second preset weight, the second resource task correlation coefficient, the experience of the execution team in performing this type of task, and the urgency of resource shortage.

[0118] In this way, a first dynamic weight and a second dynamic weight can be set based on a first preset weight, a second preset weight, the number of times the first type of group successfully executes tasks, the number of times the second type of group successfully executes tasks, the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type of resources, and the remaining amount of the second type of resources. During the calculation process, the first dynamic weight and the second dynamic weight can be set based on four aspects: the inherent weight of resources to task type, the correlation between resources and task stage, the richness of task experience of the execution group, and the urgency of resource shortage. This improves the comprehensiveness and accuracy of the first dynamic weight and the second dynamic weight.

[0119] According to an embodiment of the present invention, in step S6, a control optimization scheme is determined based on the task initial information, the real-time environment information, the first dynamic weight, and the second dynamic weight.

[0120] Figure 5 An exemplary schematic diagram illustrating the determination of a control optimization scheme according to an embodiment of the present invention is shown.

[0121] According to an embodiment of the present invention, step S6 includes:

[0122] Step S61: Determine the real-time scheduling distance and real-time location information based on the real-time environmental information;

[0123] Step S62: Determine the task type based on the initial task information;

[0124] Step S63: Determine the scheduling difficulty coefficient based on the real-time location information and the task type;

[0125] Step S64: Determine the priority scheduling coefficient based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight;

[0126] Step S65: Determine an optimized control scheme based on the priority scheduling coefficient and the task type.

[0127] For example, based on real-time environmental information, the real-time location information of the execution team is determined, and based on the real-time location information, the real-time dispatch distance is determined; based on the initial task information, the task type is determined (e.g., covert reconnaissance task, engineering construction task); based on the real-time location information and task type, the dispatch difficulty coefficient is determined. For example, based on the real-time location information, the real-time location identification result is determined, such as whether the execution team is in the training adversary's controlled area or the friendly control area. If it is in the friendly control area, the real-time location identification result is 0; otherwise, the real-time location identification result is 1. Based on the task type, the task type identification result is determined. For example, when the task type is a "covert reconnaissance task" or an "engineering construction task," which have certain requirements for resource scheduling methods (e.g., covert reconnaissance task), the task type identification result is determined. A scheduling method with high concealment must be selected (for engineering construction tasks, a scheduling method with large carrying capacity must be selected). The task type identification result is 1, otherwise it is 0. The scheduling difficulty coefficient is determined by adding the real-time location identification result and the task type identification result. The priority scheduling status of each execution group is evaluated based on the real-time scheduling distance, scheduling difficulty coefficient, first dynamic weight and second dynamic weight, and the priority scheduling coefficient is determined. Based on the priority scheduling coefficient and task type, an optimized control scheme is determined. For example, the priority scheduling coefficients of multiple execution groups are sorted in descending order to obtain the corresponding sequence. The scheduling order is determined according to the sequence. And based on the task type, precautions in the scheduling process are determined. For example, when the task type is "covert reconnaissance mission", a scheduling method with high concealment must be selected.

[0128] According to an embodiment of the present invention, step S65 includes: determining the priority scheduling coefficient of the k-th execution group at the t-th moment of the control cycle according to formula (3). ,

[0129] (3)

[0130] Where max is the function for finding the maximum value. Let e ​​be the first dynamic weight of the first type of resource in the k-th execution group at time t of the control cycle. Let r be the second dynamic weight of the r-th type of resource in the k-th execution group at time t of the control cycle. Let be the scheduling difficulty coefficient of the k-th execution group at time t of the control cycle. Let K be the real-time scheduling distance of the k-th execution group at time t of the control cycle, where K is the number of execution groups, k≤K, E is the number of first-type resources, e≤E, R is the number of second-type resources, r≤R, and e, E, r, R, k, and K are all positive integers.

[0131] According to one embodiment of the present invention, To obtain the maximum value of the first dynamic weight of the E-th type-1 resource in the k-th execution group at time t of the control cycle and the second dynamic weight of the R-th type-2 resources, the above-described maximum value extraction process can be used to determine the importance of the most important resource for the k-th execution group. The ratio represents the relative importance of the most important resource for the k-th execution group compared to the average importance of the most important resources for all K execution groups. A larger ratio indicates greater relative importance of the most important resource for the k-th execution group, and resources will be prioritized for that execution group. This is the ratio of the real-time scheduling distance of the k-th execution group at time t of the control period to the average real-time scheduling distance of the K execution groups at time t of the control period. The larger this ratio is, the farther the real-time scheduling distance of the k-th execution group is at time t of the control period, and the more resources (e.g., time, transportation resources) are spent on resource scheduling for this group. Let be the ratio of the sum of the scheduling difficulty coefficient and the resources spent on resource scheduling for the k-th execution group at time t of the control cycle to the total resources spent on resource scheduling. This indicates that a higher scheduling difficulty coefficient (meaning the execution group has a greater difficulty in acquiring resources independently) or fewer resources spent on resource scheduling prioritizes resource scheduling for that execution group. This indicates that the priority scheduling coefficient is determined based on the relative importance of the most important resources for the k-th execution group, the scheduling difficulty coefficient, and the resources spent on resource scheduling.

[0132] In this way, the priority scheduling coefficient can be determined based on the real-time scheduling distance, scheduling difficulty coefficient, first dynamic weight and second dynamic weight. During the calculation process, the priority scheduling coefficient can be determined based on the relative importance of the most important resources for the kth execution group, the scheduling difficulty coefficient and the resources spent on resource scheduling, thus improving the accuracy of the priority scheduling coefficient.

[0133] According to an embodiment of the present invention, the resource command and control optimization method can predict the first and second predicted consumption amounts of the first and second types of resources carried by each execution group based on the real-time environmental information and real-time execution status of each execution group. Based on the real-time execution status, initial task information, task execution group information, first and second predicted consumption amounts, the real-time importance of the first and second types of resources is evaluated, and a first and second dynamic weights are set. Furthermore, based on the initial task information, real-time environmental information, first and second dynamic weights, a control optimization scheme is determined, thereby replenishing resources for each execution group while minimizing the impact on the task, and improving the convenience of each execution group when performing the task. When determining the first and second predicted consumption amounts, the amounts can be determined based on the consumed amount of the first type of resources, the consumption rate of the first type of resources, the task execution rate, the task execution progress, the consumed amount of the second type of resources, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency. During the calculation process, the impact of the predicted remaining task time, the baseline consumption rate, the difficulty coefficient of the real-time task environment, and the task execution efficiency on the first predicted consumption amount, as well as the impact of the predicted number of sudden events and the consumption amount of a single sudden event on the second type of resources on the second predicted consumption amount, can be accurately analyzed. Based on the above impacts and the consumed amount of the first and second type of resources, the first and second predicted consumption amounts are determined, thus improving the accuracy of determining the first and second predicted consumption amounts. When setting the first and second dynamic weights, the first and second dynamic weights can be determined based on the first preset weight, the second preset weight, the number of times the first type of group successfully executed tasks, the number of times the second type of group successfully executed tasks, the task correlation coefficient of the first resource, the task correlation coefficient of the second resource, the remaining amount of the first type of resource, and the remaining amount of the second type of resource. During the calculation process, the first and second dynamic weights can be set based on four aspects: the inherent weight of resources to task types, the correlation between resources and task stages, the experience level of the execution group, and the urgency of resource shortages. This improves the comprehensiveness and accuracy of the first and second dynamic weights. When determining the priority scheduling coefficient, the priority scheduling coefficient can be determined based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight. During the calculation process, the priority scheduling coefficient can be determined based on the relative importance of the most important resource for the k-th execution group, the scheduling difficulty coefficient, and the resources spent on resource scheduling. This improves the accuracy of the priority scheduling coefficient.

[0134] Figure 6 An exemplary block diagram of a resource command and control optimization system according to an embodiment of the present invention is shown, the system comprising:

[0135] The initial information module is used to obtain initial task information and task execution team information;

[0136] The type setting module is used to set the resource consumption type, wherein the resource consumption type includes: a first type of resource and a second type of resource;

[0137] The real-time information module is used to acquire real-time environmental information and real-time execution status of each execution team at multiple points in the control cycle.

[0138] The consumption prediction module is used to determine the first predicted consumption and the second predicted consumption of the first type of resources and the second type of resources based on the real-time environmental information and the real-time execution status.

[0139] The weight setting module is used to set a first dynamic weight and a second dynamic weight based on the real-time execution status, the task initial information, the task execution team information, the first predicted consumption, and the second predicted consumption.

[0140] The scheme determination module is used to determine a control optimization scheme based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight.

[0141] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0142] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A resource command and control optimization method, characterized in that, include: Obtain initial task information and task execution team information; Define resource consumption types, wherein the resource consumption types include: a first type of resource and a second type of resource; At multiple points in the control cycle, acquire real-time environmental information and real-time execution status of each execution team; Based on the real-time environmental information and the real-time execution status, determine the first and second predicted consumption amounts of the first type of resources and the second type of resources; Based on the real-time execution status, the initial task information, the task execution team information, the first predicted consumption, and the second predicted consumption, a first dynamic weight and a second dynamic weight are set. Based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight, a control optimization scheme is determined. Based on the real-time execution status, the initial task information, the task execution team information, the first predicted consumption, and the second predicted consumption, a first dynamic weight and a second dynamic weight are set, including: Based on the initial task information, determine the first preset weight and the second preset weight; Based on the task execution group information, determine the number of times the first type of group successfully executed tasks and the number of times the second type of group successfully executed tasks; Based on the real-time execution status, determine the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type of resource, and the remaining amount of the second type of resource; Based on the first preset weight, the second preset weight, the number of times the first type of group successfully executed tasks, the number of times the second type of group successfully executed tasks, the first resource task correlation coefficient, the second resource task correlation coefficient, the remaining amount of the first type of resources, and the remaining amount of the second type of resources, set the first dynamic weight and the second dynamic weight. Based on the first preset weight, the second preset weight, the number of times the first type of group successfully executed tasks, the number of times the second type of group successfully executed tasks, the first resource-task correlation coefficient, the second resource-task correlation coefficient, the remaining amount of the first type of resources, and the remaining amount of the second type of resources, a first dynamic weight and a second dynamic weight are set, including: according to the formula: Determine the first dynamic weight of the e-th type of resource at time t of the control period. and the second dynamic weight of the r-th second type of resource ,in, The first preset weight for the e-th first type resource, The second preset weight is the r-th second type resource. The first resource task correlation coefficient is the first type of resource at time t of the control period. The second resource task correlation coefficient is the second type resource at time t of the control period. The number of times the first type of group successfully executed the task. The number of times the second type of group successfully performed the task. To preset the number of successful attempts, The remaining amount of the first type of resource at time t of the control period is the amount of the first type of resource remaining. The remaining amount of the second type of resource at time t of the control period is the amount of the second type of resource remaining. For the first control cycle The first predicted consumption of the e-th type of resource at time e. For the first control cycle The second predicted consumption of the second type of resource at time r.

2. The resource command and control optimization method according to claim 1, characterized in that, Define resource consumption types, including: Define a first type of resource, wherein the consumption of the first type of resource is strongly correlated with the task time; A second type of resource is defined, wherein the consumption of the second type of resource is strongly correlated with unexpected situations in the task.

3. The resource command and control optimization method according to claim 1, characterized in that, Based on the real-time environmental information and the real-time execution status, determine the first predicted consumption and the second predicted consumption of the first type of resources and the second type of resources, including: Based on the real-time execution status, obtain the amount of first type of resources consumed, task execution progress, amount of second type of resources consumed, and number of sudden events. Based on the real-time environment information, determine the difficulty coefficient of the real-time task environment; Determine the task execution efficiency based on the task execution progress; The consumption rate of the first type of resources is determined based on the amount of the first type of resources consumed and the task execution progress. The first predicted consumption and the second predicted consumption are determined based on the consumption amount of the first type of resources, the consumption rate of the first type of resources, the task execution progress, the consumption amount of the second type of resources, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency.

4. The resource command and control optimization method according to claim 3, characterized in that, Based on the real-time environment information, determine the real-time task environment difficulty coefficient, including: Based on the real-time environmental information, determine the real-time terrain, real-time temperature, and real-time weather; Based on the real-time terrain, determine the real-time terrain difficulty coefficient; Based on the real-time temperature, determine the real-time temperature difficulty coefficient; Based on the real-time weather, determine the real-time weather difficulty coefficient; The real-time task environment difficulty coefficient is determined based on the real-time terrain difficulty coefficient, the real-time temperature difficulty coefficient, and the real-time weather difficulty coefficient.

5. The resource command and control optimization method according to claim 3, characterized in that, Based on the consumed amount of the first type of resource, the consumption rate of the first type of resource, the task execution progress, the consumed amount of the second type of resource, the number of sudden events, the difficulty coefficient of the real-time task environment, and the task execution efficiency, the first predicted consumption and the second predicted consumption are determined, including: according to the formula: Determine the first control cycle The first predicted consumption of the first type of resource at time e. and the first control cycle Second predicted consumption of the second type of resource at time r ,in, To predict the time point, This refers to the amount of the first type of resource consumed at time t of the control period for the e-th first type of resource. To control the task execution progress at time t of the control cycle To control the consumption rate of the first type of resource at time t of the control period, To determine the real-time task environment difficulty coefficient at time t of the control cycle. To control the task execution efficiency at time t of the control cycle. This refers to the amount of second-type resource consumed at time t of the control period for the r-th second-type resource. This is the number of sudden events that occur at time t of the control period.

6. The resource command and control optimization method according to claim 1, characterized in that, Based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight, a control optimization scheme is determined, including: Based on the real-time environmental information, determine the real-time scheduling distance and real-time location information; Based on the initial task information, determine the task type; The scheduling difficulty coefficient is determined based on the real-time location information and the task type; The priority scheduling coefficient is determined based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight. An optimized control scheme is determined based on the priority scheduling coefficient and the task type.

7. The resource command and control optimization method according to claim 6, characterized in that, The priority scheduling coefficient is determined based on the real-time scheduling distance, the scheduling difficulty coefficient, the first dynamic weight, and the second dynamic weight, including: according to the formula: Determine the priority scheduling coefficient of the k-th execution group at time t of the control cycle. Where max is the function for finding the maximum value. Let e ​​be the first dynamic weight of the first type of resource in the k-th execution group at time t of the control cycle. Let r be the second dynamic weight of the r-th type of resource in the k-th execution group at time t of the control cycle. Let be the scheduling difficulty coefficient of the k-th execution group at time t of the control cycle. Let K be the real-time scheduling distance of the k-th execution group at time t of the control cycle, where K is the number of execution groups, k≤K, E is the number of first-type resources, e≤E, R is the number of second-type resources, r≤R, and e, E, r, R, k, and K are all positive integers.

8. A resource command and control optimization system, characterized in that, For performing the method of any one of claims 1-7, comprising: The initial information module is used to obtain initial task information and task execution team information; The type setting module is used to set the resource consumption type, wherein the resource consumption type includes: a first type of resource and a second type of resource; The real-time information module is used to acquire real-time environmental information and real-time execution status of each execution team at multiple points in the control cycle. The consumption prediction module is used to determine the first predicted consumption and the second predicted consumption of the first type of resources and the second type of resources based on the real-time environmental information and the real-time execution status. The weight setting module is used to set a first dynamic weight and a second dynamic weight based on the real-time execution status, the task initial information, the task execution team information, the first predicted consumption, and the second predicted consumption. The scheme determination module is used to determine a control optimization scheme based on the initial task information, the real-time environment information, the first dynamic weight, and the second dynamic weight.

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