An Optimization Scheduling Method and System Based on Satellite Data

Abstract

The present application discloses an optimized scheduling method and system based on satellite data, relating to the technical field of task scheduling. The method includes obtaining historical risk regions, comprehensively statistically analyzing the risk-induced routes of environmental events according to the different adjacent continuous and jump-discontinuous positional relationships among the risk regions in the historical risk regions to obtain an induced route set, formulating a task scheduling plan set according to the centralized routes, decentralized routes, first centralized then decentralized routes, and first decentralized then centralized routes in the statistically obtained induced route set, predicting the risk region route induced by the current origin environmental event according to the detected current origin environmental event and current environmental data, so as to match an optimized scheduling matching plan from the task scheduling plan set. The optimized scheduling method and system based on satellite data provided by the present invention can improve the efficiency of satellite regional information monitoring work.

Description

Technical Field

[0001] This application relates to the technical field of task scheduling, and particularly to an optimized scheduling method and system based on satellite data. Background Art

[0002] Using the data obtained from satellites and the historically monitored data for feature analysis plays an important role in aspects such as scheduling task priorities, improving task execution efficiency, enhancing decision-making support capabilities, reducing operating costs, and promoting coordinated development in multiple fields.

[0003] In traditional technologies, the optimized scheduling of task priorities based on satellite data often only makes judgments and analyzes the risks of environmental events based on the single feature of the geographical environment of the region, and formulates task priority decisions for satellite monitoring data information. It does not fully consider the associated impacts between many regions and the diverse characteristics of the trends of environmental events for differential analysis, resulting in a relatively low practicality of the formulated results of task priority decisions for satellite monitoring data information and reducing the efficiency of decision-making work. Summary of the Invention

[0004] To overcome the deficiencies of the above-mentioned prior art, this application provides an optimized scheduling method and system based on satellite data.

[0005] In a first aspect, an optimized scheduling method based on satellite data provided by this application includes:

[0006] S1. Obtain historical risk regions, and comprehensively count the risk-induced routes of environmental events to obtain an induced route set according to the different adjacent continuous and jump-discontinuous position relationships among the risk regions in the historical risk regions.

[0007] S2. Formulate a task scheduling plan set according to the centralized routes, decentralized routes, first centralized then decentralized routes, and first decentralized then centralized routes in the induced route set obtained by statistics.

[0008] S3. According to the detected current origin environmental event and current environmental data, predict the first induced prediction route of the risk region route induced by the current origin environmental event. If there is only one prediction route in the first induced prediction route, match the optimized scheduling matching plan from the task scheduling plan set.

[0009] S4. If there are at least two prediction routes in the first induced prediction route, then predict the second induced prediction route according to the preprocessed risk correlation coefficients between all the regions to be monitored obtained by statistics, and extract the third induced prediction route from the first induced prediction route and the second induced prediction route to match the optimized scheduling matching plan from the task scheduling plan set.

[0010] Preferably, obtain the origin of historical monitored environmental events and the induced historical risk regions. If there is only an adjacent and continuous positional relationship among the risk regions in the historical risk regions, then count the environmental event risk induction routes to obtain the first induction route;

[0011] If there is only a discontinuous and jumping positional relationship among the risk regions in the historical risk regions, then count the second induction route;

[0012] If there is an adjacent and continuous positional relationship among some of the risk regions in the historical risk regions, and a discontinuous and jumping positional relationship among some other risk regions, and the adjacent and continuous positional relationship is before the discontinuous and jumping positional relationship, then there is the third induction route;

[0013] If there is a discontinuous and jumping positional relationship among some of the risk regions in the historical risk regions, and an adjacent and continuous positional relationship among some other risk regions, and the discontinuous and jumping positional relationship is before the adjacent and continuous positional relationship, then there is the fourth induction route;

[0014] The first induction route, the second induction route, the third induction route and the fourth induction route are combined into an induction route set.

[0015] Preferably, count the four types of routes, namely centralized, decentralized, first centralized then decentralized, and first decentralized then centralized, in the induction route set to obtain a centralized route, a decentralized route, a first centralized then decentralized route and a first decentralized then centralized route;

[0016] Calculate the average value of the continuous duration of satellite monitoring for all risk regions with adjacent and continuous positional relationships in the first induction route and the third induction route to obtain the first monitoring duration. The first monitoring duration refers to the continuous duration of satellite monitoring for each risk region. The centralized route and the first monitoring duration are combined into the first monitoring and scheduling plan;

[0017] Calculate the average value of the continuous duration of the longest satellite monitoring for two of the risk regions with discontinuous and jumping positional relationships in the second induction route and the fourth induction route to obtain the second monitoring duration. The second monitoring duration refers to the continuous duration of satellite monitoring for each risk region.

[0018] Preferably, the first monitoring duration is implemented before the second monitoring duration. The first centralized then decentralized route, the first monitoring duration and the second monitoring duration are combined into the third monitoring and scheduling plan;

[0019] The second monitoring duration is implemented before the first monitoring duration. The first decentralized then centralized route, the first monitoring duration and the second monitoring duration are combined into the fourth monitoring and scheduling plan;

[0020] The first monitoring and scheduling plan, the second monitoring and scheduling plan, the third monitoring and scheduling plan and the fourth monitoring and scheduling plan are aggregated into a task scheduling plan set.

[0021] Preferably, historical environmental data and historical origin environmental events of risk areas and safe areas monitored historically are obtained, and a first route prediction model is established according to the historical environmental data, historical origin environmental events and induced route set;

[0022] The current origin environmental event and the current environmental data of all the to-be-monitored areas that are temporarily safe are detected, and the current origin environmental event and the current environmental data are input into the first route prediction model for testing to obtain a first induced prediction route;

[0023] If there is only one prediction route in the first induced prediction route, then according to the route set condition of the first induced prediction route, a corresponding monitoring plan is matched from the task scheduling plan set, and an optimized scheduling matching plan is output.

[0024] Preferably, if there are at least two prediction routes in the first induced prediction route, then according to the historical environmental data, the to-be-detected risk correlation coefficients between historical risk areas are statistically calculated, and according to the current environmental data, the preprocessed risk correlation coefficients between all the to-be-monitored areas are statistically calculated;

[0025] A second route prediction model is established according to the to-be-detected risk correlation coefficients, historical environmental data, historical origin environmental events and induced route set, and the preprocessed risk correlation coefficients, current origin environmental event and current environmental data are input into the second route prediction model for testing to obtain a second induced prediction route;

[0026] The same prediction routes are extracted from the second induced prediction route and the first induced prediction route to obtain a third induced prediction route, and according to the route set condition of the third induced prediction route, a corresponding monitoring plan is matched from the task scheduling plan set, and an optimized scheduling matching plan is output.

[0027] In a second aspect, an optimized scheduling system based on satellite data includes:

[0028] A route statistics unit, configured to obtain historical risk areas, and comprehensively statistically calculate environmental event risk induced routes according to different adjacent continuous and jump-discontinuous position relationship situations among the risk areas in the historical risk areas to obtain an induced route set;

[0029] A plan formulation unit, configured to formulate a task scheduling plan set according to the centralized route, decentralized route, first centralized then decentralized route and first decentralized then centralized route in the induced route set obtained by statistics;

[0030] Scenario matching unit 1 is used to predict the induced prediction route 1 of the risk area route induced by the detected current origin environment event and current environment data. If there is only one prediction route in the induced prediction route 1, an optimized scheduling matching scheme is matched from the task scheduling scheme set;

[0031] Scenario matching unit 2 is used to judge that if there are at least two prediction routes in the induced prediction route 1, then according to the preprocessed risk correlation coefficients between all the areas to be monitored statistically, the induced prediction route 2 is predicted, and the induced prediction route 3 is extracted from the induced prediction route 1 and the induced prediction route 2, so as to match an optimized scheduling matching scheme from the task scheduling scheme set.

[0032] Compared with the prior art, the present invention has the following characteristics and beneficial effects:

[0033] By statistically analyzing the trend characteristics of the induced area routes formed by the historical risk areas monitored by historical satellites and classifying different induced routes discriminatively, that is, four types: one is that the induced routes between risk areas are adjacent and continuous, one is jump-discontinuous, one is first adjacent and continuous and then jump-discontinuous, and the other is first jump-discontinuous and then adjacent and continuous. By statistically analyzing the above four induced routes, it is convenient to formulate four different satellite monitoring duration schemes for each risk area. The monitoring duration is formulated according to the route concentration status of various induced routes in the statistically analyzed induced route set. The formulated monitoring duration and the induced route set characteristics are combined into a task scheduling scheme set of the corresponding satellite monitoring task route. By formulating task scheduling schemes according to different environment events, the efficiency and orderliness of satellite monitoring work can be improved, so as to match the characteristic information of the current environment event detected subsequently. In order to reduce the accuracy of the prediction result of the risk-induced route caused by the detected current origin environment event, verification processing is carried out, that is, two induced prediction routes are predicted by different prediction methods for comparison, and the same induced prediction route is used as the finally predicted induced route, and a corresponding optimized scheduling matching scheme is matched from the previously formulated task scheduling scheme set. Through the above processing method, the orderliness of the entire information processing is improved, the utilization rate of historical feature data resources is increased, and the efficiency of satellite monitoring work is improved. Brief Description of the Drawings

[0034] Figure 1 It is a step block diagram of an optimization scheduling method based on satellite data mainly embodied in this embodiment.

[0035] Figure 2 It is a structural block diagram of an optimization scheduling system based on satellite data mainly embodied in this embodiment. Detailed Embodiment

[0036] The present invention will be further described in detail below in conjunction with the following embodiments.

[0037] Referring to Figure 1 , an optimized scheduling method based on satellite data, the method comprising the following steps:

[0038] S1. Obtain historical risk regions, and comprehensively count the environmental event risk induction routes according to the different adjacent continuous and jump-discontinuous position relationships among the risk regions in the historical risk regions to obtain an induction route set.

[0039] S2. According to the centralized routes, decentralized routes, first centralized then decentralized routes, and first decentralized then centralized routes in the counted induction route set, formulate a task scheduling plan set.

[0040] S3. According to the detected current origin environmental event and current environmental data, predict an induced prediction route 1 for the route of the current origin environmental event inducing risk regions. If there is only one prediction route in the induced prediction route 1, match an optimized scheduling matching plan from the task scheduling plan set.

[0041] S4. If there are at least two prediction routes in the induced prediction route 1, then predict an induced prediction route 2 according to the preprocessed risk correlation coefficients between all the regions to be monitored counted, extract an induced prediction route 3 from the induced prediction route 1 and the induced prediction route 2, so as to match an optimized scheduling matching plan from the task scheduling plan set.

[0042] Specifically, by statistically analyzing the trend characteristics of the induced area routes formed by historical risk areas monitored by historical satellites and classifying different induced routes discriminatively, namely four types: one is that the induced routes between risk areas are adjacent and continuous, one is discontinuous and jumping, one is adjacent and continuous first and then discontinuous and jumping, and the other is discontinuous and jumping first and then adjacent and continuous. By counting the above four induced routes, it is convenient to formulate four different satellite monitoring duration schemes for each risk area. By formulating the monitoring duration according to the route concentration status of various induced routes in the induced route set, the formulated monitoring duration and the induced route set characteristics are combined into a corresponding task scheduling scheme set for the satellite monitoring task route. By formulating the task scheduling scheme according to different environmental events, the efficiency and organization of satellite monitoring work can be improved, so as to match the characteristic information of the current environmental events detected subsequently. In order to reduce the accuracy of the predicted results of the risk-induced routes caused by the detected current origin environmental events, verification processing is carried out, that is, two induced prediction routes are predicted by different prediction methods for comparison, and the same induced prediction route is used as the finally predicted induced route, and a corresponding optimized scheduling matching scheme is matched from the previously formulated task scheduling scheme set. Through the above processing method, the organization of the entire information processing is improved, the utilization rate of historical feature data resources is increased, and the efficiency of satellite monitoring work is enhanced.

[0043] Specifically, step S1 includes the following sub-steps:

[0044] Obtain the origin of the environmental event monitored historically and the induced historical risk areas. If there is only an adjacent and continuous positional relationship between the risk areas in the historical risk areas, then count the environmental event risk-induced route to obtain the induced route one.

[0045] If there is only a discontinuous and jumping positional relationship between the risk areas in the historical risk areas, then count the induced route two.

[0046] If there is an adjacent and continuous positional relationship between some of the risk areas in the historical risk areas and a discontinuous and jumping positional relationship between some other risk areas, and the adjacent and continuous positional relationship is before the discontinuous and jumping positional relationship, then the induced route three.

[0047] If there is a discontinuous and jumping positional relationship between some of the risk areas in the historical risk areas and an adjacent and continuous positional relationship between some other risk areas, and the discontinuous and jumping positional relationship is before the adjacent and continuous positional relationship, then the induced route four.

[0048] The induced route one, the induced route two, the induced route three, and the induced route four are combined into an induced route set.

[0049] Specifically, for historical risk areas (if they are six risk areas A, B, C, D, E, and F respectively: the geographical location of the previous area is successively before that of the subsequent area, and the time of risk induction is also earlier for the previous area than the subsequent area), induction route one (such as the six risk areas A - B - C - D - E - F are all adjacent and continuous to each other, and there are no other safe areas between them), induction route two (such as the six risk areas A - B - C - D - E - F are all discontinuous and jumping, for example, there is a safe area A1 between A and B, and a safe area A2 between B and C, and so on for C and D, D and E, E and F), induction route three (such as A - B - C: A and B, B and C are all adjacent and continuous to each other, C - D - E - F: C and D, D and E, E and F are all discontinuous and jumping), induction route four (such as A - B - C: A and B, B and C are all discontinuous and jumping, C and D, D and E, E and F are all adjacent and continuous to each other).

[0050] Specifically, step S2 includes the following sub - steps:

[0051] Statistically analyze the four types of routes in the induction route set, namely centralized route, decentralized route, first - centralized - then - decentralized route, and first - decentralized - then - centralized route, to obtain the centralized route, decentralized route, first - centralized - then - decentralized route, and first - decentralized - then - centralized route respectively.

[0052] Calculate the average value of the continuous duration of satellite monitoring for all risk areas with adjacent and continuous positional relationships in induction route one and induction route three to obtain monitoring duration one. Monitoring duration one refers to the continuous duration of satellite monitoring for each risk area. The centralized route and monitoring duration one are combined into monitoring and scheduling plan one.

[0053] Calculate the average value of the continuous duration of the longest satellite monitoring for two of the risk areas with jumping - interval positional relationships in induction route two and induction route four to obtain monitoring duration two. Monitoring duration two refers to the continuous duration of satellite monitoring for each risk area. The decentralized route and monitoring duration two are combined into monitoring and scheduling plan two.

[0054] Specifically, for example, a concentrated route (here, the concentration status of the induced route is judged based on the degree of straightness of the trend of the induced route: for example, induced route one: if the included angle formed by the straight connection of three regions A - B - C is J1, and the same analysis is performed on other regions. If J1 is the maximum included angle, and the maximum degree judgment threshold for determining whether the included angle is a concentrated condition is J0, and if J1 is less than J0, then induced route one is judged as a concentrated route), a dispersed route (such as induced route two: if the included angle formed by the straight connection of three regions A - B - C is J2, and the same analysis is performed on other regions. If J2 is the minimum included angle, and if J2 is greater than or equal to J0, then induced route two is judged as a dispersed route), a first - concentrated - then - dispersed route (such as induced route three: if the included angle formed by the straight connection of three regions A - B - C is J1, the included angle formed by the straight connection of three regions C - D - E is J2, and the included angle formed by the straight connection of three regions D - E - F is J3. If J2 is less than J3, and J1 is less than J0, J2 is greater than or equal to J0, then induced route three is judged as a first - concentrated - then - dispersed route), a first - dispersed - then - concentrated route (such as induced route four: if the included angle formed by the straight connection of three regions A - B - C is J1, the included angle formed by the straight connection of three regions C - D - E is J2, and the included angle formed by the straight connection of three regions D - E - F is J3. If J2 is less than J3, and J1 is greater than J0, J3 is less than J0, then induced route four is judged as a first - dispersed - then - concentrated route), monitoring duration one (that is, taking the average of the satellite monitoring durations of the three regions A, B, and C of induced route one and the satellite monitoring durations of the three regions A, B, and C of induced route three. If they are t1, t2, t3, t4, t5, t6 respectively, then T1 = (t1 + t2 + t3 + t4 + t5 + t6) / 6, which is the monitoring duration one. Subsequently, the continuous satellite monitoring duration for each risk region is T1. For example, the continuous satellite monitoring duration for each risk region involved in the concentrated route is T1), monitoring and scheduling plan one (such as the combination of the concentrated route (induced route one) and the monitoring duration one (T1) forms monitoring and scheduling plan one, which is N1), monitoring duration two (that is, taking the average of the longest satellite monitoring durations of one of the three regions D, E, and F of induced route two and the longest satellite monitoring durations of one of the three regions D, E, and F of induced route four. If they are t7 and t8 respectively, then T2 = (t7, t8) / 2, which is the monitoring duration two. Subsequently, the continuous satellite monitoring duration for each risk region is T2. For example, the continuous satellite monitoring duration for each risk region involved in the dispersed route is T2), monitoring and scheduling plan two (such as the combination of the dispersed route (induced route two) and the monitoring duration two (T2) forms monitoring and scheduling plan two, which is N2).

[0055] Specifically, step S2 further includes the following sub - steps:

[0056] The monitoring duration one is implemented before the monitoring duration two. First, the route of "collect first and then distribute", the monitoring duration one, and the monitoring duration two are combined to form the monitoring and scheduling plan three.

[0057] The monitoring duration two is implemented before the monitoring duration one. First, the route of "distribute first and then collect", the monitoring duration one, and the monitoring duration two are combined to form the monitoring and scheduling plan four.

[0058] The monitoring and scheduling plan one, the monitoring and scheduling plan two, the monitoring and scheduling plan three, and the monitoring and scheduling plan four are aggregated into a task scheduling plan set.

[0059] Specifically, for example, in the monitoring and scheduling plan three (the monitoring duration one is implemented before the monitoring duration two: for example, the continuous monitoring duration of the risk areas A, B, and C involved in the route of "collect first and then distribute" by satellite is T1, and the continuous monitoring duration of the risk areas D, E, and F involved in the route of "collect first and then distribute" by satellite is T2. If the route of "collect first and then distribute" (induced route three), the monitoring duration one (T1), and the monitoring duration two (T2) are combined to form the monitoring and scheduling plan three as N3), and in the monitoring and scheduling plan four (the monitoring duration two is implemented before the monitoring duration one: for example, the continuous monitoring duration of the risk areas A, B, and C involved in the route of "distribute first and then collect" by satellite is T2, and the continuous monitoring duration of the risk areas D, E, and F involved in the route of "distribute first and then collect" by satellite is T1. If the route of "distribute first and then collect" (induced route four), the monitoring duration one (T1), and the monitoring duration two (T2) are combined to form the monitoring and scheduling plan three as N4).

[0060] The specific step S4 includes the following sub-steps:

[0061] Obtain the historical environmental data and historical origin environmental events of the risk areas and safe areas in the historical monitoring, and establish the route prediction model one according to the historical environmental data, historical origin environmental events, and the induced route set.

[0062] Detect the current environmental data to which the current origin environmental event and all the to-be-monitored areas that are temporarily safe belong, and input the current origin environmental event and the current environmental data into the route prediction model one for testing to obtain the induced prediction route one.

[0063] If there is only one prediction route in the induced prediction route one, then according to the route concentration condition in the induced prediction route one, match a corresponding monitoring plan from the task scheduling plan set and output the optimized scheduling matching plan.

[0064] Specifically, for environmental data (including: topography, soil type, vegetation coverage, water body distribution, meteorological conditions, human activities, the environmental data of each region is comprehensively evaluated here, that is, evaluated according to the degree values of risk generation that can be induced by each type of factor data in the environmental data. If they are x1, x2, x3, x4, x5, x6 respectively, then the comprehensive evaluation risk induction degree value is (x1 + x2 + x3 + x4 + x5 + x6) / 6 = X), Route Prediction Model 1 (such as establishing a feature information matching table for three types of feature information: historical environmental data, historical origin environmental events, and induced route sets, such as H-S-L, where H refers to historical environmental data, S refers to historical origin environmental events, and L refers to the induced routes in the induced route set), Induced Prediction Route 1 (that is, matching the current origin environmental event and the current environmental data with the established feature information matching table above to match an induced route in the induced route set. If it is L1, it is Induced Prediction Route 1). If there is only one prediction route in Induced Prediction Route 1 (if L1 is Induced Route 1, then according to the route concentration status of Induced Prediction Route 1: if it is a concentrated route, then match a corresponding monitoring plan N1 from the task scheduling plan set (N1, N2, N3, N4), which is the optimized scheduling matching plan. Subsequently, the satellite conducts data monitoring routes and monitoring durations for each region according to the routes and durations set by N1 for monitoring work).

[0065] Specifically, step S5 includes the following sub-steps:

[0066] If there are at least two prediction routes in Induced Prediction Route 1, then according to the historical environmental data, count the risk correlation coefficients to be measured between historical risk regions, and according to the current environmental data, count the preprocessed risk correlation coefficients between all regions to be monitored.

[0067] According to the risk correlation coefficients to be measured, historical environmental data, historical origin environmental events, and induced route sets, establish Route Prediction Model 2, and input the preprocessed risk correlation coefficients, current origin environmental events, and current environmental data into Route Prediction Model 2 for testing to obtain Induced Prediction Route 2.

[0068] Extract the same prediction routes from Induced Prediction Route 2 and Induced Prediction Route 1 to obtain Induced Prediction Route 3, and according to the route concentration status of Induced Prediction Route 3, match a corresponding monitoring plan from the task scheduling plan set and output the optimized scheduling matching plan.

[0069] Specifically, if there are at least two prediction routes in the induced prediction route one (if L1 contains two: induced route two and induced route three, this prediction result is very likely to be a misjudgment or two risk-induced routes, then further verification and judgment are required). The risk correlation coefficient to be measured (for example, according to historical environmental data, the comprehensive evaluation risk-induced degree values of A and B are respectively X1 and X2, (X1 - X2) / 2 = G1, which is the risk correlation coefficient to be measured between A and B. By analogy, the same analysis is carried out for other remaining regions), the preprocessed risk correlation coefficient (that is, according to the current environmental data, the comprehensive evaluation risk-induced degree values of all regions to be monitored are evaluated to obtain the preprocessed risk correlation coefficient), route prediction model two (that is, according to the risk correlation coefficient to be measured, historical environmental data, historical origin environmental events, and the induced route set, a feature information matching table is established, such as H-S-L-G, where H refers to historical environmental data, S refers to historical origin environmental events, L refers to the induced routes in the induced route set, and G refers to the risk correlation coefficient to be measured), induced prediction route two (that is, the preprocessed risk correlation coefficient, the current origin environmental event, and the current environmental data are matched with the above-established H-S-L-G feature information matching table to match the induced prediction route two. If it is induced route two), induced prediction route three (that is, induced route two), then according to the route concentration status of induced prediction route two: if it is a dispersed route, then a corresponding monitoring plan N2 is matched from the task scheduling plan set (N1, N2, N3, N4), which is the optimized scheduling matching plan. Subsequently, the satellite's data monitoring routes and monitoring durations for each region are carried out according to the routes and durations set by N2 for monitoring work).

[0070] An optimized scheduling system based on satellite data, by applying an optimized scheduling method based on satellite data as described above, includes a route statistics unit, a plan formulation unit, a plan matching unit one, and a plan matching unit two, referring to Figure 2, the historical risk areas are obtained through the route statistics unit, and according to the different adjacent continuous and jump-discontinuous positional relationships among the risk areas in the historical risk areas, the environmental event risk-induced routes are comprehensively counted to obtain an induced route set; the task scheduling scheme set is formulated through the scheme formulation unit according to the centralized routes, decentralized routes, first centralized and then decentralized routes, and first decentralized and then centralized routes in the induced route set; through the scheme matching unit one, according to the detected current origin environmental event and current environmental data, the induced prediction route one of the risk area route induced by the current origin environmental event is predicted. If there is only one prediction route in the induced prediction route one, the optimized scheduling matching scheme is matched from the task scheduling scheme set; the scheme matching unit two judges that if there are at least two prediction routes in the induced prediction route one, then according to the preprocessed risk correlation coefficients between all the areas to be monitored, the induced prediction route two is predicted, and the induced prediction route three is extracted from the induced prediction route one and the induced prediction route two, so as to match the optimized scheduling matching scheme from the task scheduling scheme set.

[0071] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application shall be covered within the protection scope of the present application.

Claims

1. An optimized scheduling method based on satellite data, characterized in that, Including the following steps: S1. Obtain historical risk regions, and comprehensively count the environmental event risk induction routes according to the different adjacent continuous and jump-discontinuous position relationships among the risk regions in the historical risk regions to obtain an induction route set; Obtain the origin of the environmental event in historical monitoring and the induced historical risk regions. If there is only an adjacent continuous position relationship among the risk regions in the historical risk regions, then count the environmental event risk induction routes to obtain the first induction route; If there is only a jump-discontinuous position relationship among the risk regions in the historical risk regions, then count the second induction route; If there is an adjacent continuous position relationship among a part of the risk regions in the historical risk regions and a jump-discontinuous position relationship among another part of the risk regions, and the adjacent continuous position relationship is before the jump-discontinuous position relationship, then the third induction route; If there is a jump-discontinuous position relationship among a part of the risk regions in the historical risk regions and an adjacent continuous position relationship among another part of the risk regions, and the jump-discontinuous position relationship is before the adjacent continuous position relationship, then the fourth induction route; The first induction route, the second induction route, the third induction route and the fourth induction route are combined into an induction route set; S2. According to the centralized routes, decentralized routes, first centralized then decentralized routes and first decentralized then centralized routes in the statistically obtained induction route set, formulate a task scheduling plan set; S3. According to the detected current origin environmental event and current environmental data, predict the first induced prediction route of the risk region route induced by the current origin environmental event. If there is only one prediction route in the first induced prediction route, then match an optimized scheduling matching plan from the task scheduling plan set; S4. If there are at least two prediction routes in the first induced prediction route, then predict the second induced prediction route according to the preprocessed risk correlation coefficients among all the regions to be monitored obtained by statistics, and extract the third induced prediction route from the first induced prediction route and the second induced prediction route, so as to match an optimized scheduling matching plan from the task scheduling plan set.

2. The optimized scheduling method based on satellite data according to claim 1, wherein The steps of S2 are specifically: Count the four types of routes, namely centralized, decentralized, first centralized then decentralized, and first decentralized then centralized, in the induction route set to obtain the centralized route, the decentralized route, the first centralized then decentralized route and the first decentralized then centralized route; Calculate the average value of the continuous duration of satellite monitoring for all the risk regions with adjacent continuous position relationships in the first induction route and the third induction route. The monitoring duration one refers to the continuous duration of satellite monitoring for each risk region. The centralized route and the monitoring duration one are combined into the first monitoring scheduling plan; Calculate the average value of the continuous duration of the longest satellite monitoring for two of the risk regions with jump-interval position relationships in the second induction route and the fourth induction route. The monitoring duration two refers to the continuous duration of satellite monitoring for each risk region. The decentralized route and the monitoring duration two are combined into the second monitoring scheduling plan.

3. The optimized scheduling method based on satellite data according to claim 2, characterized in that The steps of S2 also include: The monitoring duration 1 is implemented before the monitoring duration 2. The first-collect-then-distribute route, the monitoring duration 1, and the monitoring duration 2 are combined to form the monitoring and scheduling plan 3; The monitoring duration 2 is implemented before the monitoring duration 1. The first-distribute-then-collect route, the monitoring duration 1, and the monitoring duration 2 are combined to form the monitoring and scheduling plan 4; The monitoring and scheduling plan 1, the monitoring and scheduling plan 2, the monitoring and scheduling plan 3, and the monitoring and scheduling plan 4 are aggregated to form a task scheduling plan set.

4. An optimized scheduling method based on satellite data according to claim 3, characterized in that, The steps of S3 are specifically as follows: Obtain the historical environmental data and historical origin environmental events of the risk areas and safe areas in the historical monitoring. According to the historical environmental data, historical origin environmental events, and the induced route set, establish the route prediction model 1; Detect the current origin environmental event and the current environmental data of all the to-be-monitored areas that are temporarily safe. Input the current origin environmental event and the current environmental data into the route prediction model 1 for testing to obtain the induced prediction route 1; If there is only one prediction route in the induced prediction route 1, according to the route concentration status of the induced prediction route 1, match a corresponding monitoring plan from the task scheduling plan set, and output the optimized scheduling matching plan.

5. The optimized scheduling method based on satellite data according to claim 4, characterized in that The steps of S4 are specifically as follows: If there are at least two prediction routes in the induced prediction route 1, then according to the historical environmental data, count the to-be-detected risk correlation coefficients between the historical risk areas, and according to the current environmental data, count the preprocessed risk correlation coefficients between all the to-be-monitored areas; According to the to-be-detected risk correlation coefficients, historical environmental data, historical origin environmental events, and the induced route set, establish the route prediction model 2. Input the preprocessed risk correlation coefficients, the current origin environmental event, and the current environmental data into the route prediction model 2 for testing to obtain the induced prediction route 2; Extract the same prediction routes from the induced prediction route 2 and the induced prediction route 1 to obtain the induced prediction route 3. According to the route concentration status of the induced prediction route 3, match a corresponding monitoring plan from the task scheduling plan set, and output the optimized scheduling matching plan.

6. An optimized scheduling system based on satellite data, characterized in that, The system is used to implement an optimization scheduling method based on satellite data according to any one of claims 1-5, including: A route statistics unit, which is used to obtain the historical risk areas, and comprehensively count the environmental event risk induced routes according to the different adjacent continuous and jump-discontinuous position relationships between the risk areas in the historical risk areas to obtain the induced route set; A plan formulation unit, which is used to formulate a task scheduling plan set according to the centralized route, decentralized route, first-collect-then-distribute route of the first-concentrate-then-disperse type, and first-distribute-then-collect route of the first-disperse-then-concentrate type in the induced route set; A plan matching unit 1, which is used to predict the induced prediction route 1 of the risk area route induced by the detected current origin environmental event and current environmental data. If there is only one prediction route in the induced prediction route 1, then match the optimized scheduling matching plan from the task scheduling plan set; The scenario matching unit two is used to judge that if there are at least two prediction routes in the induced prediction route one, then according to the preprocessed risk correlation coefficients between all the to-be-monitored areas obtained by statistics, predict the induced prediction route two, and extract the induced prediction route three from the induced prediction route one and the induced prediction route two, so as to match the optimized scheduling matching scheme from the task scheduling scheme set.

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