System and method for evaluating importance degree of war damaged equipment

Abstract

The invention discloses a battle damage equipment importance degree evaluation system and method, and the method comprises the steps: building a battle damage equipment importance degree evaluation index system, and building a function relation expression according to the relation between index levels; obtaining subjective and objective weights by using an analytic hierarchy process and an entropy weight method; determining the weight of the past moment; determining the weight of trend prediction; determining the weight of the situation mutation; respectively weighting by using different types of weights to obtain judgment matrixes under different conditions; different bias coefficients are set, and the past moment judgment matrix and the trend prediction judgment matrix are combined; different bias coefficients are set, and the past moment judgment matrix and the situation mutation judgment matrix are combined; and calculating the closeness by using a TOPSIS method, and sorting the importance of the equipment. Compared with the prior art, the weight determination is more reasonable, the dynamic change of the equipment importance degree along with the battlefield condition can be fully reflected, and the evaluation result is more in line with the battlefield reality.

Description

Technical Field

[0001] The present invention relates to the field of wartime equipment maintenance priority, and in particular to a system and method for evaluating the importance of battle-damaged equipment. Background Art

[0002] At present, the research and development and application of equipment have become the focus of research in countries around the world. More and more equipment is being deployed in military operations, showing the characteristics of diversified types, multi-functional use, and multi-domain configuration. Due to the large number of equipment deployed on the battlefield and the gradual improvement of the technical content of the equipment itself, high damage rate and high failure rate may occur in a complex battlefield environment. Therefore, evaluating the importance of damaged equipment and determining the maintenance priority of damaged equipment are of great significance to the command and decision-making of damaged equipment maintenance support.

[0003] Current research on the importance assessment of battle-damaged equipment suffers from the following major shortcomings: First, when using multiple indicators to assess the importance of battle-damaged equipment, the selection of indicators for equipment repair importance assessment is not rational, and there is a certain coupling relationship between individual indicators. Furthermore, an evaluation indicator system has not yet been established based on the correlation between the repair needs of battle-damaged equipment and its operational value. Second, while dynamic variable weight assessment considers that target attributes change with battlefield dynamics, it has not yet analyzed the impact of battlefield dynamics on equipment importance after repair. Therefore, a battle-damaged equipment importance assessment system and method are proposed to address these issues. Summary of the Invention

[0004] The purpose of the present invention is to provide, on the one hand, a system for evaluating the importance of damaged equipment, and on the other hand, a method for evaluating the importance of damaged equipment. The system and method can dynamically adjust the allocation of equipment maintenance and support tasks according to changes in the battlefield situation in the current and immediate future stages, fully consider the importance of damaged equipment after emergency repair, and within a certain time window constraint, give priority to high-importance damaged equipment for emergency repair, and restore the combat performance of the damaged equipment in batches to ensure the rationality and efficiency of the allocation of maintenance and support tasks.

[0005] To achieve this purpose, the present invention provides a system for evaluating the importance of damaged equipment, which includes:

[0006] The indicator data module is used to calculate the importance index of the corresponding battle-damaged equipment based on the indicator data of each battle-damaged equipment;

[0007] The subjective and objective weight module is used to obtain the subjective and objective combined weight vector of the importance index of the battle-damaged equipment according to the importance index of all battle-damaged equipment;

[0008] The weight adjustment module is used to assign weights to the battle-damaged equipment index data at different moments in the past to obtain a weight vector for the past moments; adjust the subjective and objective combined weight vector of the battle-damaged equipment importance index at the first moment based on the battlefield situation change trend after the battle-damaged equipment is repaired to obtain a trend prediction weight vector; and adjust the subjective and objective combined weight vector of the battle-damaged equipment importance index at the first moment based on the sudden change in the battlefield situation after the battle-damaged equipment is repaired to obtain a sudden change weight vector, where the first moment represents the moment when the equipment is prepared for repair after being damaged.

[0009] The judgment matrix combination module is used to weight the past moment weight vector with the importance index of each damaged equipment at different moments in the past to obtain a past moment judgment matrix, weight the trend prediction weight vector with the importance index of each damaged equipment at the first moment to obtain a trend prediction judgment matrix, and weight the situation mutation weight vector with the importance index of each damaged equipment at the first moment to obtain a situation mutation judgment matrix; by setting the bias coefficient, the past moment judgment matrix and the trend prediction judgment matrix are combined to obtain a past moment and trend prediction combined evaluation matrix, and the past moment judgment matrix and the situation mutation judgment matrix are combined to obtain a past moment and situation mutation combined evaluation matrix;

[0010] The importance ranking module is used to sort the importance of damaged equipment based on the combined evaluation matrix of past moments and trend predictions and the combined evaluation matrix of past moments and situation mutations.

[0011] Furthermore, the method for calculating the importance index of the corresponding battle-damaged equipment based on the index data of each battle-damaged equipment includes:

[0012] The importance of combat mission B1 is calculated based on the data of battle-damaged equipment:

[0013] B1=EF current ·EF follow ·EF synergy ,EF current ,EF follow ,EF synergy ∈[0,1]

[0014] Among them, EF current EF is the impact of the current task, indicating the degree of impact of equipment damage on the current combat subtask; follow EF is the impact degree of subsequent tasks, which indicates the degree of impact of equipment damage on subsequent combat subtasks; synergy The impact degree of the coordinated mission indicates the degree of impact of equipment damage on other coordinated combat sub-tasks.

[0015] The equipment tactical importance B2 is calculated based on the battle damage equipment data:

[0016]

[0017] Among them, LC is the criticality of deployment location, which indicates the importance of the equipment's location to battlefield control, intelligence acquisition, and firepower coverage; CP is the persistence of superior capabilities, which indicates the duration and stability of the equipment's core capabilities in the battlefield environment; TR is the threat level to the enemy, which indicates the degree of direct threat posed by the equipment to enemy targets;

[0018] The equipment capability importance B3 is calculated based on the battle damage equipment data:

[0019]

[0020] Among them, OE is the payload combat effectiveness, which indicates the actual combat effect of the weapon module carried by the equipment; AD is the autonomous decision-making capability, which indicates the level of the equipment in autonomously completing tasks such as target identification and path planning; CL is the communication link stability, which indicates the reliability and anti-interference capability of data transmission between the equipment and the command system or other equipment. CV Adjustment coefficient for equipment capability importance;

[0021] The equipment confidentiality importance B4 is calculated based on the battle damage equipment data:

[0022]

[0023] Among them, SD is the importance level of sensitive data, which indicates the importance of the data stored or transmitted by the equipment to the overall combat operation; TD is the risk level of technology leakage, which indicates the possibility that the core technology of the equipment will be cracked or imitated by the enemy; RV is the enemy recycling value, which indicates the value of the sensitive data or core technology of the equipment that can be reused after it is captured by the enemy. SV Adjust the equipment confidentiality importance coefficient;

[0024] The equipment association importance B5 is calculated based on the battle damage equipment data:

[0025]

[0026] Among them, AEN is the number of affected equipment, which indicates the number of equipment directly related to the equipment whose capabilities are affected; AED is the degree of impact of associated equipment, which indicates the severity of the weakening of the functions of other associated equipment due to equipment failure in combat; AET is the time span of associated equipment impact, which indicates the time span over which the capabilities of other associated equipment are weakened due to equipment failure, k AE The equipment-related importance adjustment coefficient;

[0027] The equipment maintenance importance B6 is calculated based on the battle damage equipment data:

[0028]

[0029] DD is the degree of damage to the equipment, indicating the severity of the current damage to the equipment, such as partial loss of function or complete paralysis; EN damage is the number of damaged parts of the equipment, EN former is the number of intact parts inherent in the equipment; ST is the equipment maintenance time, which indicates the maintenance time required to restore the normal function of the equipment; CD is the maintenance resource consumption degree, which indicates the consumption degree of comprehensive resources such as manpower, spare parts, and materials when repairing the equipment; RS expend Maintenance resources required to maintain equipment, RS former The total amount of equipment maintenance resources prepared before the war, k MV This is the equipment maintenance importance adjustment coefficient.

[0030] Furthermore, the method for obtaining the subjective and objective combined weight vector of the importance index of the damaged equipment according to all the importance indexes of the damaged equipment includes: using the hierarchical analysis method to compare and calculate the historical data of the importance index of the damaged equipment to obtain the subjective weight vector of the importance index of the damaged equipment w1=(w 11 ,w 12 ,…,w 1n ); The objective weight vector w2=(w 21 ,w 22 ,…w 2n ); According to the subjective weight vector and objective weight vector of the battle damage equipment importance index, the subjective and objective combined weight vector of the battle damage equipment importance index is obtained:

[0031] w3=0.5w1+0.5w2,

[0032] Among them, w1 is the subjective weight vector of each battle-damaged equipment importance index, w2 is the objective weight vector of each battle-damaged equipment importance index, w3 is the subjective and objective combined weight vector of each battle-damaged equipment importance index, and n is the total number of battle-damaged equipment importance indexes.

[0033] Furthermore, the method of weighting the battle damaged equipment data at different moments in the past to obtain the weight vector at the past moments includes:

[0034] The inverse form of Poisson distribution is used to weight the time series and obtain the weight vector w at the past moment p :

[0035]

[0036] w p =(w s ,w s-1 ,w s-2 ,…,w1)

[0037] Where p is the serial number of the selected past moment, p! is the factorial of the serial number of the selected past moment, and w p is the weight vector of the pth moment in the past, s is the number of selected past moments, is the constant term of the inverse form of the Poisson distribution at the pth moment, is the constant of the inverse form of Poisson distribution, and the weight vector at the past moment is w p =(w s ,w s-1 ,w s-2 ,…,w1).

[0038] Furthermore, the subjective and objective combined weight vector of the importance index of the damaged equipment at the first moment is adjusted according to the changing trend of the battlefield situation after the damaged equipment is repaired. The method for obtaining the trend prediction weight vector includes:

[0039]

[0040] w fc,kj =(w fc，k1 ,w fc,k2 ,…,w fc,kn )

[0041] Among them, θ kj is the trend prediction weight adjustment coefficient of the jth importance index of each battle damaged equipment at the kth stage; ST i is the maintenance time of the i-th equipment; is the subjective and objective weight vector of the importance index of damaged equipment at the first moment; the first moment represents the moment when equipment is ready for repair after being damaged; m is the total number of damaged equipment; n is the total number of importance indexes of damaged equipment; l is the total number of combat phases; α0 is the original angle of weight importance; α kj is the weight importance trend prediction angle of the jth importance index of each battle damaged equipment in the kth stage; α0-α kj is the weight importance trend prediction inclination angle of the jth importance index of each battle damaged equipment at the kth stage, w fc,kj is the trend prediction weight of the jth importance index of each battle damaged equipment in the kth stage, and the trend prediction weight vector is w fc,kj =(w fc，k1 ,w fc,k2 ,…,w fc,kn ).

[0042] Furthermore, the subjective and objective combined weight vector of the importance index of the damaged equipment at the first moment is adjusted according to the sudden change of the battlefield situation after the damaged equipment is repaired. The method for obtaining the sudden change weight vector includes:

[0043]

[0044] w sc,ij =(w sc,i1 ,w sc,i2 ,…,w sc,i6 )

[0045] Among them, δ ij k is the weight adjustment coefficient of the situation mutation of the i-th equipment at the j-th importance index; sc is the reward and punishment coefficient, k sc =1 is reward, k sc =-1 is punishment, is the subjective and objective weight vector of the importance index of damaged equipment at the first moment; the first moment represents the moment when equipment is ready for repair after being damaged; m is the total number of damaged equipment, n is the total number of importance indexes of damaged equipment; f ij is the value of the importance index of the i-th equipment at the j-th level; is the sum of the jth importance index values of all damaged equipment, w sc,ij is the weight of the situation mutation of the i-th equipment at the j-th importance index, and the situation mutation weight vector is w sc,ij =(w sc,i1 ,w sc,i2 ,…,w sc,i6 ).

[0046] Beneficial Effects of the Present Invention: Research on the importance assessment of battle-damaged equipment has the following major shortcomings: First, when using multiple indicators to assess the importance of battle-damaged equipment, the selection of indicators for equipment repair importance assessment is not rational, and certain coupling relationships exist between individual indicators. Furthermore, an evaluation indicator system based on the correlation between the repair needs and the operational value of battle-damaged equipment has not yet been established. Second, while dynamic variable weight assessment considers that target attributes change with battlefield dynamics, it has not yet analyzed the impact of battlefield dynamics changes on equipment importance after repair. The indicator system established by the present invention better reflects the characteristics of equipment and more closely follows battlefield dynamics. Analysis of changes in indicator attributes under different circumstances further demonstrates that the indicators can truly reflect changes in battlefield dynamics. Past indicator data has little impact on the results of equipment importance assessment and cannot accurately reflect the battlefield dynamics. The present invention weights indicators for future time periods based on the battlefield dynamics after repair, making the assessment results more consistent with the predicted dynamics and more in line with battlefield reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the indicator system structure diagram of the present invention;

[0048] Figure 2 This is the overall flow chart of the importance assessment of battle-damaged equipment according to the present invention;

[0049] Figure 3It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0051] Example 1

[0052] like Figure 3 As shown, a battle-damaged equipment importance assessment system includes:

[0053] The indicator data module is used to calculate the importance index of the corresponding battle-damaged equipment based on the indicator data of each battle-damaged equipment obtained from battlefield reconnaissance;

[0054] The subjective and objective weight module is used to obtain the subjective and objective combined weight vector of the importance index of the battle-damaged equipment according to the importance index of all battle-damaged equipment;

[0055] The weight adjustment module is used to assign weights to the battle-damaged equipment index data obtained from battlefield reconnaissance at different moments in the past to obtain a weight vector for the past moments; adjust the subjective and objective combined weight vector of the battle-damaged equipment importance index at the first moment based on the battlefield situation change trend after the battle-damaged equipment is repaired to obtain a trend prediction weight vector; and adjust the subjective and objective combined weight vector of the battle-damaged equipment importance index at the first moment based on the sudden change in the battlefield situation after the battle-damaged equipment is repaired to obtain a sudden change weight vector, where the first moment represents the moment when the equipment is prepared for repair after being damaged.

[0056] The judgment matrix combination module is used to weight the past moment weight vector with the importance index of each damaged equipment at different moments in the past to obtain a past moment judgment matrix, weight the trend prediction weight vector with the importance index of each damaged equipment at the first moment to obtain a trend prediction judgment matrix, and weight the situation mutation weight vector with the importance index of each damaged equipment at the first moment to obtain a situation mutation judgment matrix; by setting the bias coefficient, the past moment judgment matrix and the trend prediction judgment matrix are combined to obtain a past moment and trend prediction combined evaluation matrix, and the past moment judgment matrix and the situation mutation judgment matrix are combined to obtain a past moment and situation mutation combined evaluation matrix;

[0057] The importance ranking module is used to sort the importance of damaged equipment based on the combined evaluation matrix of past moments and trend predictions and the combined evaluation matrix of past moments and situation mutations.

[0058] like Figure 1As shown in the figure, the importance of damaged equipment mainly includes six indicators: combat mission importance, equipment tactical importance, equipment capability importance, equipment confidentiality importance, equipment relevance importance, and equipment maintenance importance. These six indicators enable a systematic assessment of the importance of damaged equipment. Combat mission importance is calculated based on the damaged equipment's impact on the current mission, subsequent mission impact, and coordinated mission impact, as determined by battlefield reconnaissance. Equipment tactical importance is calculated based on the damaged equipment's deployment location criticality, superior capability durability, and enemy threat level, as determined by battlefield reconnaissance. Equipment capability importance is calculated based on the damaged equipment's payload combat effectiveness, autonomous decision-making capability, and communication link stability, as determined by battlefield reconnaissance. Equipment confidentiality importance is calculated based on the damaged equipment's sensitive data importance level, technology leakage risk level, and enemy recycling value, as determined by battlefield reconnaissance. Equipment relevance importance is calculated based on the number of affected relevance equipment, the degree of affected relevance equipment, and the duration of affected relevance equipment, as determined by battlefield reconnaissance. Equipment maintenance importance is calculated based on the damage level, equipment repair time, and maintenance resource consumption of the damaged equipment, as determined by battlefield reconnaissance.

[0059] Figure 2 The overall flow chart of battle-damaged equipment importance assessment is as follows: first, an indicator system (battle-damaged equipment importance indicator) is established to determine the subjective and objective weights of the battle-damaged equipment importance indicator; weights are assigned to the battle-damaged equipment indicator data obtained from battlefield reconnaissance at different times in the past to obtain past-time weights; trend prediction weights and situation mutation weights are determined based on the subjective and objective weights of the battle-damaged equipment importance indicator; a past-time judgment matrix is obtained based on the past-time weights, a trend prediction judgment matrix is obtained based on the trend prediction weights, and a situation mutation judgment matrix is obtained based on the situation mutation weights; the past-time judgment matrix is combined with the trend prediction judgment matrix to obtain a past-time and trend prediction combined evaluation matrix, and the past-time judgment matrix is combined with the situation mutation judgment matrix to obtain a past-time and situation mutation combined evaluation matrix; the TOPSIS method is used to calculate the closeness of each battle-damaged equipment indicator and rank the battle-damaged equipment importance.

[0060] In the above technical solution, the method for calculating the importance index of corresponding battle-damaged equipment based on the index data of each battle-damaged equipment obtained from battlefield reconnaissance includes:

[0061] The importance of combat mission B1 is calculated based on the damaged equipment data obtained from battlefield reconnaissance:

[0062] B1=EF current ·EF follow ·EF synergy ,EF current ,EF follow ,EF synergy ∈[0,1]

[0063] Among them, EF current EF is the impact of the current task, indicating the degree of impact of equipment damage on the current combat subtask; follow EF is the impact degree of subsequent tasks, which indicates the degree of impact of equipment damage on subsequent combat subtasks; synergy The collaborative mission impact indicates the degree of impact of equipment damage on other collaborative combat sub-tasks. The current mission impact, subsequent mission impact, and collaborative mission impact are derived through comprehensive analysis of the wartime intelligence reconnaissance system and the equipment management information system.

[0064] The equipment tactical importance B2 is calculated based on the damage equipment data obtained from battlefield reconnaissance:

[0065]

[0066] Among them, LC is the criticality of the deployment location, which indicates the importance of the equipment's location to battlefield control, intelligence acquisition, and firepower coverage; CP is the persistence of superior capabilities, which indicates the duration and stability of the equipment's core capabilities in the battlefield environment; TR is the threat level to the enemy, which indicates the degree of direct threat posed by the equipment to enemy targets; the criticality of the deployment location, the persistence of superior capabilities, and the threat level to the enemy are obtained through a comprehensive analysis of the wartime intelligence reconnaissance system and the equipment management information system.

[0067] The equipment capability importance B3 is calculated based on the battle damage equipment data obtained from battlefield reconnaissance:

[0068]

[0069] Among them, OE is the payload combat effectiveness, which indicates the actual combat effect of the weapon module carried by the equipment; AD is the autonomous decision-making capability, which indicates the level of the equipment in autonomously completing tasks such as target identification and path planning; CL is the communication link stability, which indicates the reliability and anti-interference capability of data transmission between the equipment and the command system or other equipment. CV k is the equipment capability importance adjustment coefficient; CV The value is determined based on the stability of the communication link. The higher the stability of the communication link, the higher the k CV The larger the value, the CV The value of can be but not limited to 2 or 3. In some embodiments, since the equipment needs to maintain real-time communication with the command organization or equipment operator when it exerts its combat effectiveness, if the communication link is unstable, the combat effectiveness of the equipment will be greatly reduced. Therefore, the stability of the communication link is divided into three levels from high to low: 1, 2 and 3, and the equipment capability importance is calculated respectively. When the communication link stability is level 1, the equipment capability importance adjustment coefficient k CVThe value of is 3. When the communication link stability is level 2 or level 3, the equipment capability importance adjustment coefficient k CV The value of is 2. The payload combat effectiveness, autonomous decision-making capability and communication link stability are obtained through comprehensive analysis of the wartime intelligence reconnaissance system and the equipment management information system.

[0070] The equipment confidentiality importance B4 is calculated based on the damaged equipment data obtained from battlefield reconnaissance:

[0071]

[0072] Among them, SD is the importance level of sensitive data, which indicates the importance of the data stored or transmitted by the equipment to the overall combat operation; TD is the risk level of technology leakage, which indicates the possibility that the core technology of the equipment will be cracked or imitated by the enemy; RV is the enemy recycling value, which indicates the value of the sensitive data or core technology of the equipment that can be reused after it is captured by the enemy. SV k is the equipment confidentiality importance adjustment coefficient; SV The value is determined according to the enemy's recycling value, k SV The value of can be, but is not limited to, 1 or 2. In some embodiments, the confidentiality value of equipment after combat damage is determined by the value of enemy recycling. If the core technology of the equipment is high and the importance level of sensitive data is high, the enemy cannot decipher it after stealing it or cannot convert it into combat value that is beneficial to it, and the confidentiality value of the equipment will also drop significantly. Therefore, the enemy recycling value is divided into two levels, 1 and 2, from high to low, and the confidentiality value of the equipment is calculated respectively. When the enemy recycling value is divided into level 1 or level 2, the equipment confidentiality importance adjustment coefficient k is SV The values of are also 2 and 1 respectively. The importance level of sensitive data, the risk level of technology leakage and the value of enemy recycling are obtained through comprehensive analysis of the wartime intelligence reconnaissance system and the equipment management information system.

[0073] The equipment relevance importance B5 is calculated based on the battle damage equipment data obtained from battlefield reconnaissance:

[0074]

[0075] Among them, AEN is the number of affected equipment, which indicates the number of equipment directly related to the equipment whose capabilities are affected; AED is the degree of impact of associated equipment, which indicates the severity of the weakening of the functions of other associated equipment due to equipment failure in combat; AET is the time span of associated equipment impact, which indicates the time span over which the capabilities of other associated equipment are weakened due to equipment failure, k AE k is the equipment association importance adjustment coefficient; AEThe value of can be but not limited to 10. The number of related equipment affected, the degree of related equipment affected, and the time of related equipment affected are obtained through comprehensive analysis of the wartime intelligence reconnaissance system and the equipment management information system.

[0076] The equipment maintenance importance B6 is calculated based on the damaged equipment data obtained from battlefield reconnaissance:

[0077]

[0078] DD is the degree of damage to the equipment, indicating the severity of the current damage to the equipment, such as partial loss of function or complete paralysis; EN damage is the number of damaged parts of the equipment, EN former is the number of intact parts inherent in the equipment; ST is the equipment maintenance time, which indicates the maintenance time required to restore the normal function of the equipment; CD is the maintenance resource consumption degree, which indicates the consumption degree of comprehensive resources such as manpower, spare parts, and materials when repairing the equipment; RS expend Maintenance resources required to maintain equipment, RS former The total amount of equipment maintenance resources prepared before the war, k MV k is the equipment maintenance importance adjustment coefficient. MV The value of can be, but is not limited to, 0.3. The degree of equipment damage, equipment repair time, and maintenance resource consumption are determined through a comprehensive analysis of the wartime intelligence reconnaissance system and the equipment management information system.

[0079] It should be noted that the calculation formulas for combat mission importance, equipment tactical importance, equipment capability importance, equipment confidentiality importance, equipment correlation importance, and equipment maintenance importance are obtained based on historical experience summary and data fitting.

[0080] In the above technical solution, the method for obtaining the subjective and objective combined weight vector of the importance index of the damaged equipment according to the importance index of all damaged equipment includes: using the hierarchical analysis method to compare and calculate the historical data of the importance index of the damaged equipment to obtain the subjective weight vector of the importance index of the damaged equipment w1=(w 11 ,w 12 ,…,w 1n ); The objective weight vector w2=(w 21 ,w 22 ,…w 2n ); According to the subjective weight vector and objective weight vector of the battle damage equipment importance index, the subjective and objective combined weight vector of the battle damage equipment importance index is obtained:

[0081] w3=0.5w1+0.5w2,

[0082] Among them, w1 is the subjective weight vector of each battle-damaged equipment importance index, w2 is the objective weight vector of each battle-damaged equipment importance index, w3 is the subjective and objective combined weight vector of each battle-damaged equipment importance index, and n is the total number of battle-damaged equipment importance indexes.

[0083] The hierarchical analysis method is to combine historical data to compare the importance of equipment importance indicators, assign the importance of indicators according to the scale of 1-9, and construct the relative importance judgment matrix A = (a rj ) n×n Then the eigenvalue method is used to solve the maximum eigenvalue λ of the relative importance judgment matrix A max and its corresponding eigenvector τ, normalized eigenvector w 1i ,Finally, the consistency ratio CR is used to test the consistency of the judgment matrix, and the subjective weight vector is w1.

[0084] Aτ=λ max τ

[0085]

[0086] w1=(w 11 ,w 12 ,…,w 1n )

[0087] Among them, a rj is the importance scale of the battle damage equipment importance index r to the battle damage equipment importance index j, and satisfies a rr =1,a jr =1 / a rj ;λ max is the maximum eigenvalue; τ is the eigenvector corresponding to the maximum eigenvalue; w 1r is the rth normalized eigenvector; CI is the consistency index; n is the matrix order, which is also the total number of importance indicators of damaged equipment; RI is the random consistency index, which is related to the matrix order n and is usually obtained by looking up the table; CR is the consistency ratio. If CR < 0.1, the judgment matrix is considered to have satisfactory consistency, otherwise the judgment matrix needs to be readjusted.

[0088] The entropy weight method establishes a judgment matrix based on the value of the importance index of the damaged equipment, normalizes the judgment matrix C, and generates a normalized judgment matrix F = (fi j ) m×n , then calculate the information entropy value based on the value of the battle damage equipment importance index, and finally calculate the objective weight based on the value of the battle damage equipment importance index to obtain the objective weight vector w2:

[0089]

[0090] w2=(w21 ,w 22 ,…w 2n )

[0091] Among them, c ij is the original value of the jth indicator of the i-th equipment, f ij is the normalized value of the jth indicator of the i-th equipment; e j is the information entropy value of the jth indicator; w 2j is the objective weight of the jth indicator, m is the total number of damaged equipment, and n is the total number of importance indicators of damaged equipment.

[0092] The hierarchical analysis method constructs a subjective weight vector through historical data to reflect the importance of indicators and domain knowledge, ensuring that the weight of the importance indicator of battle-damaged equipment conforms to combat reality. The entropy weight method calculates the information entropy of the importance indicator data of battle-damaged equipment based on the degree of variation of the importance indicator data of battle-damaged equipment, eliminates human bias, and integrates subjective and objective weights. This can avoid the one-sidedness of purely subjective judgment and overcome the problem that the weight distribution results of the importance indicator of battle-damaged equipment due to pure data analysis are out of touch with reality.

[0093] In the above technical solution, the method of weighting the battle-damaged equipment data obtained from battlefield reconnaissance at different moments in the past to obtain the weight vector of the past moments includes: selecting the battle-damaged equipment data at different moments in the past, and improving the comprehensiveness of the battle-damaged equipment importance data by weighting the moments. On the battlefield, the closer the battle-damaged equipment importance index parameter is to the current moment, the more it can reflect the importance of the equipment and the higher the accuracy of the evaluation. Therefore, the index parameter close to the current moment should be given a larger moment weight. In some embodiments, the time interval t p As the span, select the current time t1 and the previous time t1-(s-1)t p As a complete research sequence, the time series is weighted using the inverse form of Poisson distribution to obtain the past moment weight vector w p .

[0094] The inverse form of Poisson distribution is used to weight the time series and obtain the weight vector w at the past moment p :

[0095]

[0096] w p =(w s ,w s-1 ,w s-2 ,…,w1)

[0097] Where p is the serial number of the selected past moment, p! is the factorial of the serial number of the selected past moment, and w p is the weight vector of the pth moment in the past, s is the number of selected past moments, is the constant term of the inverse form of the Poisson distribution at the pth moment, is the constant of the inverse form of Poisson distribution, and the weight vector at the past moment is w p =(w s ,w s-1 ,w s-2 ,…,w1).

[0098] In the above technical solution, the subjective and objective combined weight vector of the importance index of the damaged equipment at the first moment is adjusted according to the trend of battlefield situation changes after the damaged equipment is repaired. The method for obtaining the trend prediction weight vector includes: since the equipment repair time is different, the time of reactivation after repair is not at the same time, the situation changes at different times after repair are different, and the weight variables of the importance index of each damaged equipment are also different, it is necessary to divide the weight changes of different stages and different repair times. This solution mainly considers the impact of the stage of executing combat missions on the importance of equipment, and divides the combat stage of equipment executing combat missions into three stages: mission preparation, mission implementation and mission completion. According to the law of battlefield situation development, the fixed equipment repair time t d The combat mission is divided into three stages, which allows the importance assessment of damaged equipment to adapt to changes in the battlefield situation. In the calculation process, mission preparation is set as the first stage, mission implementation is set as the second stage, and mission completion is set as the third stage.

[0099]

[0100] w fc,kj =(w fc，k1 ,w fc,k2 ,…,w fc,kn )

[0101] Among them, θ kj is the trend prediction weight adjustment coefficient of the jth importance index of each battle damaged equipment at the kth stage; ST i is the maintenance time of the i-th equipment; is the subjective and objective weight vector of the importance index of damaged equipment at the first moment. The first moment represents the moment when equipment is ready for repair after being damaged. m is the total number of damaged equipment. n is the total number of importance indexes of damaged equipment. l is the total number of combat phases (i.e., mission preparation, mission implementation, and mission completion). α0 is the original angle of weight importance, which is determined by the commander's judgment. α kj is the weight importance trend prediction angle of the jth importance index of each battle damaged equipment in the kth stage, α kj Derived from reconnaissance information analysis; α0-α kj is the weight importance trend prediction inclination angle of the jth importance index of each battle damaged equipment at the kth stage, w fc,kjis the trend prediction weight of the jth importance index of each battle damaged equipment in the kth stage, and the trend prediction weight vector is w fc,kj =(w fc，k1 ,w fc,k2 ,…,w fc,kn ).

[0102] In the above technical solution, the subjective and objective weight vector of the first moment's damaged equipment importance index is adjusted based on the sudden change in battlefield situation after repair of damaged equipment. The method for deriving the weight vector for the sudden change in situation includes the following: When the enemy adjusts its combat deployment, whether the current combat mission and equipment tactical deployment are aligned with the enemy's tactical requirements after the adjustment is particularly important, significantly impacting the combat mission importance and equipment tactical importance. Positive impacts are rewarded, while negative impacts are penalized. When the enemy implements electromagnetic interference, the equipment's communication capabilities are disrupted. Whether the equipment can stably execute combat missions and coordinate properly with related equipment is crucial. Rewards or penalties should be applied based on the degree of impact on equipment capabilities and equipment interdependencies. When windy and rainy weather occurs, the equipment's deployment location, capability performance, and maintenance operations are all impacted. The weights of the equipment's tactical importance, equipment capability importance, and equipment maintenance importance should be adjusted, and corresponding rewards or penalties should be applied. By adjusting the weights, the damaged equipment importance assessment can better adapt to sudden changes such as enemy tactics and weather conditions.

[0103]

[0104] w sc,ij =(w sc,i1 ,w sc,i2 ,…,w sc,i6 )

[0105] Among them, δ ij k is the weight adjustment coefficient of the situation mutation of the i-th equipment in the j-th indicator; sc is the reward and punishment coefficient, k sc =1 is reward, k sc =-1 is punishment, is the subjective and objective weight vector of the importance index of damaged equipment at the first moment. The first moment represents the moment when equipment is ready for repair after being damaged. m is the total number of damaged equipment, and n is the total number of importance indexes of damaged equipment. f ij is the value of the importance index of the i-th equipment at the j-th level, is the sum of the jth index values of all damaged equipment, w sc,ij is the weight of the situation mutation of the i-th equipment in the j-th indicator, and the situation mutation weight vector is w sc,ij =(w sc,i1 ,w sc,i2 ,…,w sc,i6 ).

[0106] In the above technical solution, the weight vector of the past moment and the importance index of each damaged equipment at different moments in the past are weighted to obtain the past moment judgment matrix F s The methods include:

[0107]

[0108] Among them, m is the total number of damaged equipment, n is the total number of importance indicators of damaged equipment, F p is the judgment matrix of the pth moment in the past, s is the number of selected past moments, w p The past moment judgment matrix is used to reflect the importance of damaged equipment at different combinations of past moments. The past moment judgment matrix integrates historical data to reflect the changes in equipment importance over time, ensuring that the importance assessment of damaged equipment meets the needs of dynamic battlefield changes.

[0109] The trend prediction judgment matrix F is obtained by weighting the trend prediction weight vector and the value of each equipment importance index at the first moment. fc The methods include:

[0110] F fc =(F1w fc ) m×n

[0111] Among them, m is the total number of damaged equipment, n is the total number of importance indicators of damaged equipment, F1 is the matrix formed by the weighting of the subjective and objective weight vectors and the value of each equipment importance indicator at the first moment. The first moment represents the moment when the equipment is ready to be repaired after being damaged in battle, w fc The trend prediction weight vector is used to represent the importance of each piece of equipment under normal conditions as predicted by the first moment. The trend prediction judgment matrix, based on the first moment data and superimposed on the trend prediction weight vector, reflects the dynamic changes in the repair priority of damaged equipment through data.

[0112] The situation mutation weight vector is weighted with the value of each equipment importance index at the first moment to obtain the situation mutation judgment matrix F sc The methods include:

[0113] F sc =(F1w sc ) m×n

[0114] Among them, m is the total number of damaged equipment, n is the importance index of damaged equipment, F1 is the matrix formed by the weighting of the subjective and objective weight vectors and the importance index values of each equipment at the first moment, w scThe situational mutation weight vector is used by the situational mutation judgment matrix to reflect the importance of each piece of equipment under future situational mutations, as predicted from the first moment. The situational mutation judgment matrix, based on the first moment data and superimposed on the situational mutation weight vector, provides an early reflection of sudden battlefield events, ensuring that the repair priority of damaged equipment aligns with the latest battlefield situation.

[0115] In the above technical solution, the method for ranking the importance of damaged equipment according to the combined evaluation matrix of past time and trend prediction and the combined evaluation matrix of past time and situation mutation includes:

[0116] By setting the bias coefficient, the past moment judgment matrix and the trend prediction judgment matrix are combined to obtain the past moment and trend prediction combined judgment matrix F ch1 :

[0117] F ch1 =γF fc +(1-γ)F s

[0118] Among them, F fc is the trend prediction judgment matrix, γ is the bias coefficient of the combined judgment matrix of past moments and trend prediction, F s is the past moment judgment matrix, and the values of the bias coefficient γ include but are not limited to 0, 0.3, 0.5, 0.7, and 1. The past moment and trend forecast combined judgment matrix sets the bias coefficient based on historical experience, combines the past moment judgment matrix and the trend forecast judgment matrix, and makes the importance assessment of battle-damaged equipment more accurate.

[0119] Combine the past moment judgment matrix and the situation mutation judgment matrix to obtain the past moment and situation mutation combined judgment matrix F ch2 ;

[0120] F ch2 =εF sc +(1-ε)F s

[0121] Among them, ε is the bias coefficient of the combined evaluation matrix of the past moment and the situation mutation, F sc is the situation mutation judgment matrix, F s is the past moment judgment matrix, and the values of the bias coefficient ε include but are not limited to 0, 0.3, 0.5, 0.7, and 1. The bias coefficient of the past moment and situation mutation combination judgment matrix is set based on historical experience. The past moment judgment matrix and the situation mutation combination judgment matrix are combined to make the importance assessment of damaged equipment more accurate.

[0122] The TOPSIS method is used to rank equipment importance based on the combined evaluation matrix of past time and trend prediction, and the combined evaluation matrix of past time and situation mutation. The higher the equipment importance is ranked, the higher the maintenance priority of the equipment after combat damage.

[0123] The TOPSIS method, or the superior-inferior solution distance method, determines the positive ideal solution (the optimal value for each indicator) and the negative ideal solution (the worst value for each indicator) based on a combined evaluation matrix of past moments and trend forecasts and a combined evaluation matrix of past moments and sudden situation changes. The relative proximity between the positive and negative ideal solutions is calculated and ranked to obtain a ranking of equipment importance. Using the TOPSIS method to rank equipment importance, the importance of each damaged piece of equipment at different points in the past is combined with the importance of current battlefield trends and sudden changes. This allows for a comprehensive assessment of the importance of damaged equipment, ensuring that the assessment results are realistic and highly accurate.

[0124] Example 2: A preferred embodiment:

[0125] In a local conflict, 5 pieces of equipment were damaged. The serial numbers of the 5 damaged pieces of equipment were E1, E2, E3, E4, and E5. In order to determine the priority of equipment repair, k CV Take 2, k SV If t is 1 and s is 3, there are three past moments in time, and the importance of five pieces of equipment needs to be assessed. The secondary indicator data for the five pieces of equipment at time t1 is shown in Table 1. The secondary indicator data for times t2 and t3 are omitted. The importance of damaged equipment is assessed using the past moments, trend prediction, and comprehensive weighting of sudden situation changes, along with the TOPSIS method.

[0126] Table 1 Secondary indicator data of importance of damaged equipment at time t1

[0127]

[0128] Step 1: Calculate the first-level equipment importance index (i.e., the importance index of battle-damaged equipment) at t1, t2, and t3

[0129] According to Table 1, using the calculation formula of the six first-level indicators, the second-level indicator data of the three past moments t1, t2, and t3 (i.e., the three influencing factors corresponding to the importance index of each battle-damaged equipment, see Figure 1 ) to calculate and obtain the judgment matrix (c ij ) 5×6 , see Table 2-Table 4.

[0130] Table 2 Data on importance of damaged equipment at time t1

[0131]

[0132] Table 3 Data on importance of damaged equipment at time t2

[0133]

[0134] Table 4 Importance index data of damaged equipment at time t3

[0135]

[0136] Step 2: Determine the subjective and objective combined weight vector of each damage equipment importance index

[0137] 1) Determine the subjective weight vector

[0138] After expert review, the relative importance of the six first-level indicator attributes was obtained, and the comparison matrix A was obtained.

[0139]

[0140] According to the AHP method, the subjective weights of each indicator in matrix A are solved to form the subjective weight vector:

[0141] w1=(0.1602,0.3807,0.2516,0.0425,0.0642,0.1009)

[0142] After looking up the table and calculation, the consistency ratio CR=0.0195<0.1, so the consistency test passed.

[0143] 2) Determine the objective weight vector

[0144] After normalizing the judgment matrix C of the three past moments t1, t2, and t3, we get the normalized judgment matrix F = (f ij ) 5×6 Based on the normalized judgment matrix F, the entropy weight method is used to solve the objective weight vector of each indicator:

[0145] Time t1:

[0146]

[0147] Time t2:

[0148]

[0149] Time t3:

[0150]

[0151] 3) Determine the subjective and objective combined weight vector

[0152] For the subjective weight vector w1 and the objective weight vector Combine them separately to get the subjective and objective combination weight vector

[0153]

[0154] Step 3: Calculate the weighted judgment matrix of the three past moments respectively

[0155] Multiply the equipment importance numerical matrices of the three past moments by the corresponding subjective and objective combined weight vectors to obtain: F1, F2, and F3.

[0156] Step 4: Calculate the weight vector of the past moment and construct the judgment matrix of the past moment

[0157] Based on the weighted judgment matrix of the three past moments in step 3, using the inverse form of Poisson distribution, we can get Get the weight vector w at the past moment p =(0.5333, 0.2667, 0.2000). Weight the matrices F1, F2, and F3 respectively to obtain the past moment judgment matrix F S .

[0158]

[0159] Step 5: Calculate the closeness based on the past time judgment matrix

[0160] Apply the TOPSIS method to the matrix F1, F2, F3 and the matrix F s , the distance and closeness between each equipment importance and the positive and negative ideal solutions are obtained, and the equipment importance is sorted according to the closeness. The results are listed in Table 5.

[0161] Table 5 Equipment importance ranking of 3 past moments and 3 moments fusion

[0162]

[0163] Step 6: Calculate the trend prediction weight vector based on the subjective and objective combined weight vector of the importance index of damaged equipment at time t1

[0164] Setting the fixed maintenance interval at 30 minutes and referring to equipment usage experience data, we derived the positive and negative values of the trend prediction weight inclination angles for each stage (see Table 6). Assuming the current stage is mission implementation, the trend prediction weight calculation formula was used to obtain the trend prediction weight adjustment coefficients for each piece of equipment during mission implementation (see Table 7). Furthermore, the trend prediction weights for each piece of equipment were obtained (see Table 8).

[0165] Table 6 Trend prediction weight tilt angle of each task stage

[0166]

[0167] Table 7 Weight adjustment coefficients for trend prediction of each equipment during the mission implementation phase

[0168]

[0169]

[0170] Table 8 Trend prediction weights of various equipment

[0171]

[0172] Step 7: Calculate the closeness of different bias coefficients under trend prediction and sort them

[0173] The trend prediction judgment matrix F is obtained by weighting the trend prediction weight vector and the value of each equipment importance index at time t1. fc Then, according to different bias coefficients, the past moment judgment matrix and the trend prediction judgment matrix are weighted respectively to obtain the combined judgment matrix of the past moment and trend prediction. The TOPSIS method is used to calculate the closeness and sort them respectively. The results are shown in Table 9.

[0174] Table 9: Closeness ranking of different bias coefficients under trend prediction

[0175]

[0176] Step 8: Calculate the weight vector of the sudden change of situation based on the subjective and objective combined weight vector of the importance index of the damaged equipment at time t1

[0177] According to the situation mutation weight calculation formula, the situation mutation weight adjustment coefficient of each equipment under three situation mutation conditions is calculated, and then the situation mutation weight of each equipment is calculated, see Tables 10 to 12.

[0178] Table 10 Weights of sudden situation changes when the enemy adjusts its deployment

[0179]

[0180] Table 11 Situational mutation weights under enemy electromagnetic interference

[0181]

[0182] Table 12 Weights of sudden change in situation under windy and rainy weather conditions

[0183]

[0184] Step 9: Calculate the closeness of different bias coefficients under sudden situation changes and sort them

[0185] The three situation mutation weight vectors and the values of the equipment importance index at time t1 are weighted to obtain three situation mutation judgment matrices. Then, according to different bias coefficients, the past moment judgment matrix and the three situation mutation judgment matrices are weighted to obtain three combined evaluation matrices of the past moment and situation mutation. The TOPSIS method is used to calculate the closeness of the three evaluation matrices and rank them. The results are shown in Tables 13 to 15.

[0186] Table 13: Closeness ranking of different bias coefficients when the enemy adjusts its deployment

[0187]

[0188] Table 14: Ranking of proximity of different bias coefficients under enemy electromagnetic interference

[0189]

[0190] Table 15: Ranking of closeness of different bias coefficients under windy and rainy weather conditions

[0191]

[0192]

[0193] in conclusion:

[0194] 1) As shown in Table 5, when the proximity calculation is based on the indicator data from the past three moments, the ranking results are E3 > E2 > E1 > E5 > E4. When the bias coefficient is 0, that is, the equipment importance indicator data from the past three moments is integrated, the ranking results are E3 > E2 > E1 > E5 > E4. This shows that considering only the indicator data from the past moments and assigning weights to different moments does not affect the ranking of damaged equipment. When the bias coefficients are 0.3, 0.5, 0.7, and 1, respectively, that is, considering the changes in indicator weights caused by trend development, the ranking results change to E3 > E1 > E2 > E5 > E4. The ranking of reconnaissance vehicles and strike aircraft has changed, with the reconnaissance vehicle's equipment importance shifting from third to second. This indicates that after the equipment is repaired, the battlefield situation may shift from high-intensity confrontation to low-intensity confrontation, with battlefield reconnaissance being the main focus; or our side may be adjusting its combat deployment, organizing reconnaissance forces, and switching to intermittent defense. Therefore, considering the changes in weights combined with past moments and trend predictions is more reasonable.

[0195] 2) When considering the enemy's deployment adjustment, when the bias coefficient is 0, that is, only considering the indicator data at the past moment, the ranking result is E3>E2>E1>E5>E4. When the bias coefficient gradually increases, the ranking result remains unchanged. When the bias coefficient is 1, that is, only considering the weight change caused by the enemy's adjustment of combat deployment, the ranking result changes to E2>E3>E1>E5>E4. The ranking of reconnaissance aircraft and strike aircraft has changed, and the importance of strike aircraft equipment has changed from second to first. This shows that the enemy has signs of adjusting its deployment at this stage, is organizing strike forces, and is preparing to launch a new round of attacks on us, or is about to switch from an intermittent defensive state to an offensive state. We should speed up the repair of offensive equipment to better cope with the firepower confrontation after the enemy's deployment adjustment.

[0196] 3) When considering enemy electromagnetic interference, when the bias coefficients are 0, 0.3, and 0.5, the ranking results are all E3>E2>E1>E5>E4. When the bias coefficient is 0.7, the ranking results are E3>E1>E2>E5>E4. When the bias coefficient is 1, the ranking results are E1>E3>E2>E5>E4. By comparison, when the weight change caused by enemy electromagnetic interference is increased, the rankings of reconnaissance aircraft and strike aircraft gradually move to the back, indicating that when the enemy implements electromagnetic interference, the performance of reconnaissance aircraft and strike aircraft declines and their importance decreases. The ranking of reconnaissance vehicles gradually moves to the front, indicating that reconnaissance vehicles have overcome the influence of electromagnetic interference. This equipment may have both reconnaissance and electronic countermeasure capabilities and urgently needs to be repaired to exert its electronic countermeasure advantages.

[0197] 4) Considering windy and rainy weather, when the bias coefficients are 0, 0.5, and 0.7, the ranking results are E3>E2>E1>E5>E4. When the bias coefficients are 0.3 and 1, the ranking results are E3>E2>E5>E1>E4. The proximity ranking of the submersible changes from fourth to third. From the overall data analysis, considering the change in the weight of windy and rainy weather, the proximity data of the submersible is similar to that of the reconnaissance vehicle, indicating that windy and rainy weather has little impact on the performance of the submersible and the importance of the equipment remains basically unchanged.

[0198] From the analysis of the results, considering the impact of trend prediction and three mutation situations on the equipment importance weight is in line with the equipment characteristics and the battlefield situation's requirements for equipment capabilities. Therefore, considering the impact of trend prediction and situation mutation situations, and evaluating equipment importance in a dynamic weighted manner can make the calculation results more reliable and in line with battlefield reality.

[0199] Example 3

[0200] The battle-damaged equipment importance assessment method based on the battle-damaged equipment importance assessment system includes:

[0201] Step 1: Calculate the importance index of the corresponding damaged equipment based on the damage index data obtained from battlefield reconnaissance;

[0202] Step 2: Obtain the subjective and objective combined weight vector of the importance index of damaged equipment according to the importance index of all damaged equipment;

[0203] Step 3: Weight the damaged equipment index data obtained from battlefield reconnaissance at different moments in the past to obtain a past-moment weight vector; adjust the subjective and objective combined weight vector of the damaged equipment importance index at the first moment based on the battlefield situation change trend after the damaged equipment is repaired to obtain a trend prediction weight vector; adjust the subjective and objective combined weight vector of the damaged equipment importance index at the first moment based on the sudden change in the battlefield situation after the damaged equipment is repaired to obtain a sudden change weight vector, where the first moment represents the moment when the equipment is ready for repair after being damaged;

[0204] Step 4: Weight the past moment weight vector and the importance index of each damaged equipment at different moments in the past to obtain the past moment judgment matrix, weight the trend prediction weight vector and the importance index of each damaged equipment at the first moment to obtain the trend prediction judgment matrix, and weight the situation mutation weight vector and the importance index of each damaged equipment at the first moment to obtain the situation mutation judgment matrix; by setting the bias coefficient, combine the past moment judgment matrix with the trend prediction judgment matrix to obtain the past moment and trend prediction combined evaluation matrix, and combine the past moment judgment matrix with the situation mutation judgment matrix to obtain the past moment and situation mutation combined evaluation matrix;

[0205] Step 5: Sort the importance of damaged equipment according to the combined evaluation matrix of past moments and trend prediction and the combined evaluation matrix of past moments and situation mutation.

[0206] Example 4

[0207] A computer program product includes a computer program, which implements the steps of the method described in Example 3 when executed by a processor.

[0208] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0209] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0210] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0211] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0212] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0213] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A system for evaluating the importance of battle-damaged equipment, characterized in that: It includes: The indicator data module is used to calculate the importance index of the corresponding battle-damaged equipment based on the indicator data of each battle-damaged equipment; The subjective and objective weight module is used to obtain the subjective and objective combined weight vector of the importance index of the battle-damaged equipment according to the importance index of all battle-damaged equipment; The weight adjustment module is used to assign weights to the battle-damaged equipment index data at different moments in the past to obtain a weight vector for the past moments; adjust the subjective and objective combined weight vector of the battle-damaged equipment importance index at the first moment based on the battlefield situation change trend after the battle-damaged equipment is repaired to obtain a trend prediction weight vector; and adjust the subjective and objective combined weight vector of the battle-damaged equipment importance index at the first moment based on the sudden change in the battlefield situation after the battle-damaged equipment is repaired to obtain a sudden change weight vector, where the first moment represents the moment when the equipment is prepared for repair after being damaged. The judgment matrix combination module is used to weight the past moment weight vector with the importance index of each damaged equipment at different moments in the past to obtain a past moment judgment matrix, weight the trend prediction weight vector with the importance index of each damaged equipment at the first moment to obtain a trend prediction judgment matrix, and weight the situation mutation weight vector with the importance index of each damaged equipment at the first moment to obtain a situation mutation judgment matrix; by setting the bias coefficient, the past moment judgment matrix and the trend prediction judgment matrix are combined to obtain a past moment and trend prediction combined evaluation matrix, and the past moment judgment matrix and the situation mutation judgment matrix are combined to obtain a past moment and situation mutation combined evaluation matrix; The importance ranking module is used to rank the importance of damaged equipment based on the combined evaluation matrix of past moments and trend predictions and the combined evaluation matrix of past moments and situation mutations.

2. The system for evaluating the importance of battle-damaged equipment according to claim 1, characterized in that: The method for calculating the importance index of corresponding battle-damaged equipment based on the index data of each battle-damaged equipment includes: The importance of combat mission B1 is calculated based on the data of battle-damaged equipment: B1=EF current ·IF follow ·IF synergy ,IF current ,IF follow ,IF synergy ∈[0,1] Among them, EF current EF is the impact of the current task, indicating the degree of impact of equipment damage on the current combat subtask; follow EF is the impact degree of subsequent tasks, which indicates the degree of impact of equipment damage on subsequent combat subtasks; synergy The impact degree of the coordinated mission indicates the degree of impact of equipment damage on other coordinated combat sub-tasks. The equipment tactical importance B2 is calculated based on the battle damage equipment data: Among them, LC is the criticality of deployment location, which indicates the importance of the equipment's location to battlefield control, intelligence acquisition, and firepower coverage; CP is the persistence of superior capabilities, which indicates the duration and stability of the equipment's core capabilities in the battlefield environment; TR is the threat level to the enemy, which indicates the degree of direct threat posed by the equipment to enemy targets; The equipment capability importance B3 is calculated based on the battle damage equipment data: Among them, OE is the payload combat effectiveness, which indicates the actual combat effect of the weapon module carried by the equipment; AD is the autonomous decision-making capability, which indicates the level of the equipment in autonomously completing tasks such as target identification and path planning; CL is the communication link stability, which indicates the reliability and anti-interference capability of data transmission between the equipment and the command system or other equipment. CV Adjustment coefficient for equipment capability importance; The equipment confidentiality importance B4 is calculated based on the battle damage equipment data: Among them, SD is the importance level of sensitive data, which indicates the importance of the data stored or transmitted by the equipment to the overall combat operation; TD is the risk level of technology leakage, which indicates the possibility that the core technology of the equipment will be cracked or imitated by the enemy; RV is the enemy recycling value, which indicates the value of the sensitive data or core technology of the equipment that can be reused after it is captured by the enemy. SV Adjust the equipment confidentiality importance coefficient; The equipment association importance B5 is calculated based on the battle damage equipment data: Among them, AEN is the number of affected equipment, which indicates the number of equipment directly related to the equipment whose capabilities are affected; AED is the degree of impact of associated equipment, which indicates the severity of the weakening of the functions of other associated equipment due to equipment failure in combat; AET is the time span of associated equipment impact, which indicates the time span over which the capabilities of other associated equipment are weakened due to equipment failure, k AE The equipment-related importance adjustment coefficient; The equipment maintenance importance B6 is calculated based on the battle damage equipment data: DD is the degree of damage to the equipment, indicating the severity of the current damage to the equipment, such as partial loss of function or complete paralysis; EN damage is the number of damaged parts of the equipment, EN former is the number of intact parts inherent in the equipment; ST is the equipment maintenance time, which indicates the maintenance time required to restore the normal function of the equipment; CD is the maintenance resource consumption degree, which indicates the consumption degree of comprehensive resources such as manpower, spare parts, and materials when repairing the equipment; RS expend Maintenance resources required to maintain equipment, RS former The total amount of equipment maintenance resources prepared before the war, k MV This is the equipment maintenance importance adjustment coefficient.

3. The battle-damaged equipment importance assessment system according to claim 1, characterized in that: The method for obtaining the subjective and objective combined weight vector of the importance index of the damaged equipment according to the importance index of all damaged equipment includes: using the hierarchical analysis method to compare and calculate the historical data of the importance index of the damaged equipment to obtain the subjective weight vector w1=(w 11 ,w 12 ,…,w 1n ); the objective weight vector w2=(w 21 ,w 22 ,…w 2n ); According to the subjective weight vector and objective weight vector of the battle damage equipment importance index, the subjective and objective combined weight vector of the battle damage equipment importance index is obtained: w3=0.5w1+0.5w2, Among them, w1 is the subjective weight vector of each battle-damaged equipment importance index, w2 is the objective weight vector of each battle-damaged equipment importance index, w3 is the subjective and objective combined weight vector of each battle-damaged equipment importance index, and n is the total number of battle-damaged equipment importance indexes.

4. The system for evaluating the importance of battle-damaged equipment according to claim 1, characterized in that: The method of weighting the damaged equipment data at different moments in the past to obtain the weight vector at the past moments includes: The inverse form of Poisson distribution is used to weight the time series and obtain the weight vector w at the past moment p : w p =(w s ,w s-1 ,w s-2 ,…,w1) Where p is the serial number of the selected past moment, p! is the factorial of the serial number of the selected past moment, and w p is the weight vector of the pth moment in the past, s is the number of selected past moments, is the constant term of the inverse form of the Poisson distribution at the pth moment, is the constant of the inverse form of Poisson distribution, and the weight vector at the past moment is w p =(w s ,w s-1 ,w s-2 ,…,w1).

5. A battle-damaged equipment importance assessment system according to claim 1 or 3, characterized in that: The method for obtaining the trend prediction weight vector by adjusting the subjective and objective combined weight vector of the importance index of the damaged equipment at the first moment according to the battlefield situation change trend after the damaged equipment is repaired includes: In fc,kj =(in fc，k1 ,In fc,k2 ,…,In fc,kn ) Among them, θ kj is the trend prediction weight adjustment coefficient of the jth importance index of each battle damaged equipment at the kth stage; ST i is the maintenance time of the i-th equipment; is the subjective and objective weight vector of the importance index of damaged equipment at the first moment; the first moment represents the moment when equipment is ready for repair after being damaged; m is the total number of damaged equipment; n is the total number of importance indexes of damaged equipment; l is the total number of combat phases; α0 is the original angle of weight importance; α kj is the weight importance trend prediction angle of the jth importance index of each battle damaged equipment in the kth stage; α0-α kj is the weight importance trend prediction inclination angle of the jth importance index of each battle damaged equipment at the kth stage, w fc,kj is the trend prediction weight of the jth importance index of each battle damaged equipment in the kth stage, and the trend prediction weight vector is w fc,kj =(w fc，k1 ,w fc,k2 ,…,w fc,kn ).

6. The battle-damaged equipment importance assessment system according to claim 3, characterized in that: The subjective and objective combined weight vector of the importance index of damaged equipment at the first moment is adjusted according to the sudden change of battlefield situation after the damaged equipment is repaired. The method for obtaining the sudden change weight vector includes: In sc,ij =(in sc,i1 ,In sc,i2 ,…,In sc,i6 ) Among them, δ ij k is the weight adjustment coefficient of the situation mutation of the i-th equipment at the j-th importance index; sc is the reward and punishment coefficient, k sc =1 is reward, k sc =-1 is punishment, is the subjective and objective weight vector of the importance index of damaged equipment at the first moment; the first moment represents the moment when equipment is ready for repair after being damaged; m is the total number of damaged equipment, n is the total number of importance indexes of damaged equipment; f ij is the value of the importance index of the i-th equipment at the j-th level; is the sum of the jth importance index values of all damaged equipment, w sc,ij is the weight of the situation mutation of the i-th equipment at the j-th importance index, and the situation mutation weight vector is w sc,ij =(w sc,i1 ,w sc,i2 ,…,w sc,i6 ).

7. The battle-damaged equipment importance assessment system according to any one of claims 4 to 6, characterized in that: The past moment judgment matrix F is obtained by weighting the past moment weight vector and the importance index of each damaged equipment at different moments in the past. s The methods include: Among them, m is the total number of damaged equipment, n is the total number of importance indicators of damaged equipment, F p is the judgment matrix of the pth moment in the past, s is the number of selected past moments, w p is the weight vector of the past moment; The trend prediction judgment matrix F is obtained by weighting the trend prediction weight vector and the value of each equipment importance index at the first moment. fc The methods include: F fc =(F1w fc ) m×n Among them, m is the total number of damaged equipment, n is the total number of importance indicators of damaged equipment, and F1 is the matrix formed by the weighting of the subjective and objective weight vectors and the value of each equipment importance indicator at the first moment; the first moment represents the time when equipment is ready to be repaired after being damaged in battle; w fc is the trend prediction weight vector; The situation mutation weight vector is weighted with the importance index of each equipment at the first moment to obtain the situation mutation judgment matrix F sc The methods include: F sc =(F1w sc ) m×n Among them, m is the total number of damaged equipment, n is the total number of importance indicators of damaged equipment, F1 is the matrix formed by the weighting of the subjective and objective weight vectors and the numerical values of the importance indicators of each equipment at the first moment; w sc is the weight vector of situation mutation.

8. The battle-damaged equipment importance assessment system according to claim 7, characterized in that: The methods for ranking the importance of damaged equipment based on the combined evaluation matrix of past moments and trend prediction and the combined evaluation matrix of past moments and situation mutation include: By setting the bias coefficient, the past moment judgment matrix and the trend prediction judgment matrix are combined to obtain the past moment and trend prediction combined judgment matrix F ch1 : F ch1 =γF fc +(1-γ)F s Among them, F fc is the trend prediction judgment matrix, γ is the bias coefficient of the combined judgment matrix of past moments and trend prediction, F s is the past moment judgment matrix; Combine the past moment judgment matrix and the situation mutation judgment matrix to obtain the past moment and situation mutation combined judgment matrix F ch2 ; F ch2 =εF sc +(1-ε)F s Among them, ε is the bias coefficient of the combined evaluation matrix of the past moment and the situation mutation, F sc is the situation mutation judgment matrix, F s is the past moment judgment matrix; The TOPSIS method is used to rank the equipment importance according to the combined evaluation matrix of past time and trend forecast and the combined evaluation matrix of past time and situation mutation.

9. A method for evaluating the importance of damaged equipment based on the system for evaluating the importance of damaged equipment according to any one of claims 1 to 8, comprising: The importance index of the corresponding battle-damaged equipment is calculated based on the index data of each battle-damaged equipment; According to the importance indexes of all damaged equipment, the subjective and objective combined weight vector of the importance indexes of damaged equipment is obtained; Weights are assigned to damage equipment index data at different moments in the past to obtain a past-moment weight vector; the subjective and objective combined weight vector of the damage equipment importance index at the first moment is adjusted based on the battlefield situation change trend after the damage equipment is repaired to obtain a trend prediction weight vector; the subjective and objective combined weight vector of the damage equipment importance index at the first moment is adjusted based on the sudden change in the battlefield situation after the damage equipment is repaired to obtain a sudden change weight vector, where the first moment represents the moment when the equipment is ready for repair after being damaged; The past moment weight vector is weighted with the importance index of each damaged equipment at different moments in the past to obtain the past moment judgment matrix, the trend prediction weight vector is weighted with the importance index of each damaged equipment at the first moment to obtain the trend prediction judgment matrix, and the situation mutation weight vector is weighted with the importance index of each damaged equipment at the first moment to obtain the situation mutation judgment matrix; by setting the bias coefficient, the past moment judgment matrix and the trend prediction judgment matrix are combined to obtain the past moment and trend prediction combined evaluation matrix, and the past moment judgment matrix and the situation mutation judgment matrix are combined to obtain the past moment and situation mutation combined evaluation matrix; The importance of damaged equipment is ranked according to the combined evaluation matrix of past moments and trend predictions and the combined evaluation matrix of past moments and situation mutations.

10. A computer program product comprising a computer program / instruction, which implements the steps of the method according to claim 9 when the computer program / instruction is executed by a processor.

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