A method and system for optimizing position feedback of a fusion command system

Abstract

The present invention discloses a method and system for optimizing the position feedback of a fusion command system, belonging to the field of information technology, and specifically comprising: collecting and preprocessing the position data of mobile personnel or vehicles from the system; extracting position features using linear discriminant analysis, and correcting the feature data through a map matching algorithm; calculating accurate position information using an improved position feedback algorithm, and associating it with audio and video data and a GIS map; transmitting the position information to a central node through an optimization protocol, and displaying it on an interface to achieve command dispatch and resource allocation; collecting user feedback optimization algorithms, redeploying and testing and verifying, the present invention improves the accuracy of position data and the command dispatch efficiency of the system.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and in particular to a method and system for optimizing position feedback of a fusion command system. Background Art

[0002] With the rapid development of information technology, communication and command systems have become the core of modern public security, social management and military operations. In the integrated command system, the location data of mobile personnel or vehicles is crucial for command and dispatch and resource allocation. However, in practical applications, the return of location data often faces many challenges, such as high data transmission delay, insufficient positioning accuracy, poor information security, high energy consumption, unstable transmission and poor system compatibility. These problems not only affect the efficiency of command and dispatch, but also may pose a potential threat to public safety and social order. Although there are some solutions to the above problems on the market, they can usually only partially solve a specific problem and fail to fully cover all needs. For example, increasing redundant data to improve the reliability of data transmission will lead to higher transmission delays; using encryption to protect data security will increase the computing burden and energy consumption.

[0003] For example, the Chinese patent application with publication number CN110225505A discloses a comprehensive emergency command method and system based on 4G and Beidou, including: on-site staff use terminal equipment to transmit real-time on-site information back to the command user terminal through the 4G network submodule or Beidou satellite navigation submodule; the command user terminal performs alarm analysis based on the on-site situation; the command user terminal sends the work plan and command command to the terminal equipment through the 4G network submodule or Beidou satellite navigation submodule; on-site emergency personnel receive the command command and execute it, and feed back the execution result to the command user terminal in real time. This technical solution can realize the location monitoring, management and emergency command functions of vehicles and personnel involved in public security emergency cases through the Beidou satellite navigation submodule and 4G network technology, especially in the absence of 4G network or harsh environment conditions, it can transmit on-site information in real time for emergency command and dispatch.

[0004] For example, the Chinese patent application with publication number CN118574076A discloses a method for positioning firefighters at a fire scene assisted by a smoke alarm, including: deploying a smoke alarm in a protection area, including a smoke sensor module and a positioning module; when smoke is detected, activating the positioning module and the battery power supply mode, and the positioning module periodically broadcasts a positioning signal into space; when firefighters carry a positioning terminal into the fire area, the positioning terminal receives the positioning signal broadcast by the smoke alarm in real time, obtains the RSSI value of the received signal strength, parses the MAC address of the smoke alarm, performs its own position fusion solution and Kalman filtering, and uses the filtering result as the position estimation of the positioning terminal, and transmits it back to the command center through the emergency communication network. This technical solution has the characteristics of multi-source data fusion technology, which is conducive to building a real-time positioning system for firefighters at a fire scene.

[0005] The above existing technologies all have the following problems: technical dependence and limitations; lack of in-depth analysis and processing of location data; lack of feedback and optimization mechanisms; and limitations in data transmission and display methods. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a method and system for optimizing the position feedback of a fusion command system, which collects the position data of mobile personnel or vehicles from the system and performs preprocessing; uses linear discriminant analysis to extract position features, and corrects the feature data through a map matching algorithm; uses an improved position feedback algorithm to calculate precise position information, and associates it with audio and video data and a GIS map; transmits the position information to a central node through an optimized protocol, and displays it on an interface to achieve command, dispatch and resource allocation; collects user feedback to optimize the algorithm, redeploys it and tests and verifies it. The present invention improves the accuracy of position data and the command and dispatch efficiency of the system.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for optimizing position feedback of a fusion command system, comprising:

[0009] Step S1: collecting the location data of mobile personnel or vehicles from the fusion command system and preprocessing them;

[0010] Step S2: using a linear discriminant analysis method to extract features from the preprocessed position data, obtain position feature data, and correct the position feature data;

[0011] Step S3: using the improved position return algorithm to calculate the corrected position feature data to obtain position information, and associating the obtained position information with the audio and video data and GIS map in the fusion command system;

[0012] Step S4: The calculated location information is transmitted to the central node of the fusion command system through the optimized data transmission protocol. At the same time, the location information is displayed on the interface of the fusion command system, and command dispatch and resource allocation are performed according to the location information.

[0013] Step S5: Collecting user feedback and opinions on the improved location return algorithm, optimizing the improved location return algorithm according to the feedback, obtaining an optimized location return algorithm, redeploying the optimized location return algorithm into the fusion command system, and performing testing and verification;

[0014] Specifically, the specific steps of step S3 include:

[0015] S3.1: Obtain corrected position feature data ,based on , using an improved location return algorithm Calculate and output the calculated position information, where: Indicates n Corrected position feature data, n Indicates the number of corrected position feature data;

[0016] S3.2: Obtain audio and video data from the fusion command system, and associate the audio and video data with the calculated location information based on the timestamp and device ID to obtain associated location information;

[0017] S3.3: Load GIS map data, map the associated location information onto the GIS map, determine the specific location, and display the associated audio and video data and location information;

[0018] Specifically, the specific steps of the improved position feedback algorithm in S3.1 include:

[0019] S3.11: Obtain corrected position feature data , and initialize the initial state of the Kalman filter and the covariance matrix ,in, and represents the initial position, and represents the initial velocity;

[0020] S3.12: At each time step k , using the state prediction equation and the covariance prediction equation To make a prediction, the formula is:

[0021] ;

[0022] in, Indicates k The state vector predicted before the step, represents the state transfer matrix, represents the posterior estimate of the previous moment, represents the control input matrix, represents the control input vector, represents the environmental factor matrix, represents the external interference vector;

[0023] S3.13: Obtaining real-time corrected position feature data , and is updated using the Kalman gain equation, state update equation, and covariance update equation, as follows:

[0024] ;

[0025] in, represents the posterior estimate at the current moment, represents the Kalman gain, represents the observation matrix, represents the state correction matrix;

[0026] S3.14: Output posterior estimates The location part and As the calculated position information, and Indicates the calculated speed information.

[0027] Specifically, the specific steps of step S2 include:

[0028] S2.1: Obtaining preprocessed position data ,in, Indicates a Mobile personnel location data, Indicates b Vehicle location data, a Indicates the number of mobile personnel position data, b Indicates the quantity of vehicle location data;

[0029] S2.2: Compute human position data and vehicle location data The mean vector of ,in, c =1, represents the mean vector of the mobile personnel position data, and , express The i location data,c =2, represents the mean vector of vehicle position data, and

[0030] , express The i location data, c Indicates the category of location data, Indicates category c The number of location data.

[0031] Specifically, the specific steps of step S2 also include:

[0032] S2.3: Calculate the intra-class scatter matrix and the between-class scatter matrix , the formula is:

[0033] ;

[0034] in, Represents a location data point In category c The weights in , T represents transpose, represents the regularization coefficient, Indicates category c The regularization matrix of Indicates category c The global weight of represents the mean of the preprocessed position data X;

[0035] S2.4: By maximizing the between-class scatter matrix and the within-class scatter matrix The ratio of Solving the projection matrix ,and ,in, represents the feature vector, Indicates d The transpose of the eigenvectors, d represents the number of eigenvectors, Represents the eigenvalue.

[0036] Specifically, the specific steps of step S2 also include:

[0037] S2.5: Use the projection matrix to project the preprocessed position data into a low-dimensional space to obtain position feature data;

[0038] S2.6: Load high-precision map data, and calculate the position of the current position feature data in the high-precision map according to the position feature data;

[0039] S2.7: Match the location feature data with the road and intersection structured data in the high-precision map, and calculate the error between the location feature data and the high-precision map based on the matching result;

[0040] S2.8: According to the error result, the projected position feature data is corrected using a Kalman filter algorithm to obtain corrected position feature data.

[0041] A position feedback optimization system for a fusion command system, comprising: a data acquisition module, a correction module, a position information calculation module, a data transmission module, and an optimization module;

[0042] The correction module is used to correct the extracted location feature data using a map matching algorithm;

[0043] The position information calculation module is used to calculate the corrected position feature data using an improved position return algorithm to obtain accurate position information, and associate the position information with the audio and video data and GIS map in the fusion command system;

[0044] The data transmission module is used to transmit the location information to the central node of the integrated command system through the optimized data transmission protocol, and to display the location information in real time on the system interface, and to perform command dispatch and resource allocation according to the location information;

[0045] The optimization module is used to collect user feedback and opinions on the improved location return algorithm, optimize the algorithm based on the feedback, and redeploy the optimized algorithm into the system for testing and verification.

[0046] Specifically, the correction module includes: a feature extraction unit and a correction unit;

[0047] The feature extraction unit is used to extract features from the preprocessed position data using a linear discriminant analysis method;

[0048] The correction unit is used to correct the extracted position feature data using a map matching algorithm to reduce positioning errors.

[0049] Specifically, the location information calculation module includes: a location calculation unit and a data association unit;

[0050] The position calculation unit is used to calculate the corrected position feature data using an improved position feedback algorithm to obtain position information;

[0051] The data association unit is used to associate the calculated location information with the audio and video data and the GIS map to form complete command and dispatch information.

[0052] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, steps of a method for optimizing position feedback of a fusion command system are implemented.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention proposes a position feedback optimization system for a fusion command system, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple processes, low investment and operation costs, and low production work costs.

[0055] The present invention proposes a location feedback optimization method for a fusion command system. By collecting and preprocessing location data, and using linear discriminant analysis to extract and correct features, combined with an improved location feedback algorithm, the accuracy and real-time performance of location information are significantly improved. At the same time, the location information is associated with audio and video data and GIS maps, providing intuitive and comprehensive information support for command and dispatch and resource allocation, thereby improving the overall effectiveness and decision-making efficiency of the system.

[0056] The present invention proposes a location feedback optimization method for a fusion command system, and also focuses on the collection of user feedback and algorithm optimization iteration. By continuously adjusting and optimizing the location feedback algorithm based on user opinions, the continuous improvement and adaptability of the system are ensured; the optimized data transmission protocol further guarantees the fast and reliable transmission of location information. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of a position feedback optimization method for a fusion command system of the present invention;

[0058] Figure 2 This is a principle flow chart of a method for optimizing the position feedback of a fusion command system of the present invention;

[0059] Figure 3 It is a flow chart of position feature data acquisition of a position feedback optimization method of a fusion command system of the present invention;

[0060] Figure 4 This is a system architecture diagram of position feedback optimization of a fusion command system of the present invention. DETAILED DESCRIPTION

[0061] Example 1

[0062] See also Figure 1-Figure 3 , an embodiment of the present invention provides: a method for optimizing the position feedback of a fusion command system, comprising the following steps:

[0063] Step S1: collecting the location data of mobile personnel or vehicles from the fusion command system and preprocessing them;

[0064] Furthermore, the specific steps of step S1 include:

[0065] (1) Data collection: By integrating radar, optoelectronic and other sensor equipment in the command system, or GPS positioning system, the location data of mobile personnel or vehicles can be collected in real time. During the data collection process, the integrity and accuracy of the data must be ensured to avoid data loss or errors;

[0066] (2) Data preprocessing: Preliminary processing of the collected location data, including data unpacking, information data capture, and data format processing. At the same time, outliers and noise in the data are cleaned up, such as removing duplicate data and filling missing data, to improve the accuracy and reliability of the data.

[0067] Step S2: using a linear discriminant analysis method to extract features from the preprocessed position data, obtain position feature data, and correct the position feature data;

[0068] Step S3: using the improved position return algorithm to calculate the corrected position feature data to obtain position information, and associating the obtained position information with the audio and video data and GIS map in the fusion command system;

[0069] Step S4: The calculated location information is transmitted to the central node of the fusion command system through the optimized data transmission protocol. At the same time, the location information is displayed on the interface of the fusion command system, and command dispatch and resource allocation are performed according to the location information.

[0070] Furthermore, the specific steps of step S4 include:

[0071] (1) Obtain the target’s location information, including latitude, longitude, and altitude;

[0072] (2) Select the Aspera transmission protocol and encode the location information of the target to meet the format requirements of the selected data transmission protocol;

[0073] (3) Using the optimized data transmission protocol, the encoded location information is transmitted to the central node of the fusion command system through the wireless network;

[0074] (4) At the central node of the integrated command system, the received location information is decoded and restored to the original latitude, longitude, altitude and other information;

[0075] (5) Displaying the decoded location information on the interface of the integrated command system. The location information can be displayed intuitively in the form of maps, charts, etc.;

[0076] (6) Conducting command and dispatch and allocating resources based on the displayed location information. For example, the nearest rescue team can be determined and corresponding materials and equipment can be deployed;

[0077] (7) On the interface of the integrated command system, command and dispatch instructions and information are delivered to relevant personnel and departments in real time through voice dispatch, video dispatch and other functions.

[0078] Step S5: Collect user feedback and opinions on the improved location feedback algorithm, optimize the improved location feedback algorithm based on the feedback, obtain the optimized location feedback algorithm, redeploy the optimized location feedback algorithm to the fusion command system, and perform testing and verification.

[0079] Furthermore, the specific steps of step S5 include:

[0080] (1) Design feedback collection channels: Establish user feedback collection channels through questionnaire surveys, user interviews, online forums, etc., and guide users to provide feedback and opinions on the accuracy, real-time performance, stability, etc. of the improved location feedback algorithm. At the same time, organize and analyze the collected user feedback to form a feedback report;

[0081] (2) Based on the feedback report, identify the problems and improvement directions in the improved location feedback algorithm, evaluate the impact of the problems, and determine which problems need to be solved urgently and which can be optimized later;

[0082] (3) Based on the analysis results, formulate an optimization plan for the improved location return algorithm, including algorithm adjustment and parameter optimization, and modify and improve the improved location return algorithm according to the optimization plan;

[0083] (4) After the modification is completed, internal testing is carried out to ensure that the optimized algorithm meets the requirements in terms of function and performance;

[0084] (5) Redeploy the optimized position feedback algorithm to the integrated command system to ensure that the system can operate normally. If changes are involved in the data format, data migration and conversion are required.

[0085] (6) Develop a detailed test plan based on the characteristics of the optimized algorithm, including test scenarios, test steps, and expected results, and conduct tests according to the test plan and record the test results;

[0086] (7) Analyze the test results and evaluate whether the optimized algorithm achieves the expected effect.

[0087] The specific steps of step S2 include:

[0088] S2.1: Obtaining preprocessed position data ,in, Indicates a Mobile personnel location data, Indicates b Vehicle location data, a Indicates the number of mobile personnel position data, b Indicates the quantity of vehicle location data;

[0089] S2.2: Compute human position data and vehicle location data The mean vector of ,in, c =1, represents the mean vector of the mobile personnel position data, and , express The i location data, c =2, represents the mean vector of vehicle position data, and , express The i location data, c Indicates the category of location data, Indicates category c The number of location data;

[0090] S2.3: Calculate the intra-class scatter matrix and the between-class scatter matrix , the formula is:

[0091] ;

[0092] in, Represents a location data point In category c The weights in , T represents transpose, represents the regularization coefficient, Indicates category c The regularization matrix of Indicates category c The global weight of represents the mean of the preprocessed position data X;

[0093] It should be noted that the formula in the present invention can more flexibly handle the different importance and influence in the data by introducing weight parameters, which is particularly important for processing unbalanced data sets or data points with special significance; secondly, the addition of regularization terms not only helps to prevent model overfitting and improve generalization ability, but also can smooth the noise in the data to a certain extent and enhance the robustness of the model; in addition, the formula in the present invention also takes into account the situation of multiple features or variables and introduces interaction terms, which helps to capture the complex relationships and patterns in the data and improve the accuracy and reliability of the model. In general, the formula in the present invention has stronger competitiveness and application value in processing complex data, unbalanced data and improving model performance.

[0094] S2.4: By maximizing the between-class scatter matrix and the within-class scatter matrix The ratio of Solving the projection matrix ,and ,in, represents the feature vector, Indicates d The transpose of the eigenvectors, d represents the number of eigenvectors, Represents the eigenvalue, which is a scalar that indicates when the eigenvector Matrix or When transforming, the scaling factor of its length or modulus;

[0095] S2.5: Use the projection matrix to project the preprocessed position data into a low-dimensional space to obtain position feature data;

[0096] Furthermore, the specific steps of S2.5 include:

[0097] (1) Obtaining location data and the projection matrix ;

[0098] (2) Represent the location data X in matrix form, where each row represents a location data point and each column represents a dimension;

[0099] (3) Using the projection matrix to perform a linear transformation on the position data matrix to obtain projected low-dimensional data, wherein the matrix linear transformation is a prior art in the art and is not an inventive solution of the present application, and is not described in detail here;

[0100] (4) Check whether the projected data retains important information in the original data.

[0101] S2.6: Load high-precision map data, and calculate the position of the current position feature data in the high-precision map according to the position feature data;

[0102] Furthermore, the specific steps of S2.6 include:

[0103] (1) Reading high-precision map data, which usually contains detailed geographic information such as roads, intersections, buildings, terrain, and the coordinates, shapes, and attributes of this information;

[0104] (2) Preprocessing the received location feature data;

[0105] Use a point-to-point geometric matching algorithm to match the location feature data with the high-precision map data, wherein the point-to-point geometric matching algorithm is a prior art content in this field, is not an inventive solution of the present application, and is not described in detail here;

[0106] (3) Determine the possible location of the current location feature data in the high-precision map by calculating the similarity between the location feature data and the elements such as roads and intersections in the map in terms of distance, direction, shape, etc.;

[0107] (4) Output the matching result, that is, the possible location of the current location feature data in the high-precision map, and verify the matching result to ensure the accuracy and reliability of the result. The verification is performed by comparing with known landmarks.

[0108] S2.7: Calculate the error between the location feature data and the high-precision map according to the matching result between the current location feature data and the high-precision map;

[0109] S2.8: According to the error result, the projected position feature data is corrected using the Kalman filter algorithm to obtain the corrected position feature data, wherein the Kalman filter algorithm is the prior art content in this field and is not the creative solution of the present application, and will not be elaborated here.

[0110] The specific steps of step S3 include:

[0111] S3.1: Obtain corrected position feature data ,based on , using an improved location return algorithm Calculate and output the calculated position information, where: Indicates n Corrected position feature data, n Indicates the number of corrected position feature data;

[0112] S3.2: Obtain audio and video data from the fusion command system, and associate the audio and video data with the calculated location information based on the timestamp and device ID to obtain associated location information;

[0113] Furthermore, the specific steps of S3.2 include:

[0114] (1) Obtaining audio and video data from the fused command system, which usually involves interacting with the system's audio and video interfaces to obtain real-time or stored audio and video streams;

[0115] (2) Parse the audio and video data and extract key information such as timestamps and device IDs. This information is usually embedded in the metadata of the audio and video data and requires corresponding parsing algorithms to extract;

[0116] (3) Query the location information of the corresponding device based on the device ID or other relevant information, which requires interacting with the location information database to obtain the real-time location or historical location data of the device;

[0117] If the device itself has a positioning function, such as GPS or Beidou, the location information can be obtained directly from the device;

[0118] If the device does not have a positioning function, it can be positioned through network base station positioning or WiFi positioning;

[0119] (4) using a data association algorithm to associate the timestamp and device ID of the extracted audio and video data with the calculated location information to ensure that each audio and video data segment corresponds to the correct location information. The data association algorithm is a prior art in the art and is not an inventive solution of the present application, and is not described in detail herein.

[0120] (5) The associated audio and video data and location information are stored in a database for subsequent analysis and display.

[0121] S3.3: Load GIS map data, map the associated location information onto the GIS map, determine the specific location, and display the associated audio and video data and location information.

[0122] Furthermore, the specific steps of S3.3 include:

[0123] (1) Load the required GIS map data from local or online resources. This data usually includes geographic coordinates, terrain, buildings, roads and other layers, and ensure that the associated location information, that is, the location markers in the audio and video data have been effectively associated with the audio and video data. The associated location information usually exists in the form of longitude and latitude, coordinate points or other geographic references;

[0124] (2) Use the positioning tools or plug-ins in the GIS software to import the associated location information into the GIS map, and ensure that the associated location information is consistent with the geographic coordinate system of the GIS map through coordinate conversion or projection matching;

[0125] (3) On the GIS map, based on the associated location information, find and mark the geographical location corresponding to the audio and video data. The present invention uses the annotation tool in the GIS software to achieve this;

[0126] (4) Add playback controls or links for audio and video data on the GIS map, and associate the audio and video data with specific locations on the GIS map so that users can view and play the audio and video content directly on the map. At the same time, detailed information of the location information, such as address, coordinates, surrounding facilities, etc., can be displayed on the map.

[0127] The specific steps of the improved location feedback algorithm in S3.1 include:

[0128] S3.11: Obtain corrected position feature data , and initialize the initial state of the Kalman filter and the covariance matrix ,in, and represents the initial position, and represents the initial velocity;

[0129] S3.12: At each time step k , using the state prediction equation and the covariance prediction equation To make a prediction, the formula is:

[0130] ;

[0131] in, Indicates k The state vector predicted before the step, that is, k The prior estimate of the step, represents the state transfer matrix, represents the posterior estimate of the previous moment, represents the control input matrix, represents the control input vector, Represents the environmental factor matrix, considering the impact of environmental changes on the state, represents the external interference vector;

[0132] S3.13: Obtaining real-time corrected position feature data , and is updated using the Kalman gain equation, state update equation, and covariance update equation, as follows:

[0133] ;

[0134] in, represents the posterior estimate at the current moment, Represents the Kalman gain, which is used to adjust the impact of the measurement value on the state estimation. represents the observation matrix, represents the state correction matrix, which is used to further adjust the state estimation;

[0135] S3.14: Output posterior estimates The location part and As the calculated position information, and Indicates the calculated speed information.

[0136] Example 2

[0137] See also Figure 4 Another embodiment provided by the present invention is a position feedback optimization system for integrating a command system, comprising:

[0138] Data acquisition module, correction module, position information calculation module, data transmission module, optimization module;

[0139] The data collection module is used to collect the location data of mobile personnel or vehicles from the fusion command system and perform preliminary processing and cleaning to ensure the accuracy and availability of the data;

[0140] A correction module, used to correct the extracted location feature data using a map matching algorithm to improve the accuracy of positioning;

[0141] The location information calculation module is used to calculate the corrected location feature data using the improved location return algorithm to obtain accurate location information, and associate the location information with the audio and video data and GIS map in the fusion command system;

[0142] The data transmission module is used to transmit the location information to the central node of the integrated command system through the optimized data transmission protocol, and display the location information in real time on the system interface, and perform command dispatch and resource allocation based on the location information;

[0143] The optimization module is used to collect user feedback and opinions on the improved location return algorithm, optimize the algorithm based on the feedback, and redeploy the optimized algorithm into the system for testing and verification.

[0144] The correction module includes: a feature extraction unit and a correction unit;

[0145] A feature extraction unit, used for extracting features from the preprocessed position data using a linear discriminant analysis method;

[0146] The correction unit is used to correct the extracted position feature data using a map matching algorithm to reduce positioning errors.

[0147] The location information calculation module includes: a location calculation unit and a data association unit;

[0148] A position calculation unit, used to calculate the corrected position feature data using an improved position feedback algorithm to obtain accurate position information;

[0149] The data association unit is used to associate the calculated location information with the audio and video data and the GIS map to form complete command and dispatch information.

[0150] The data transmission module includes: a data transmission unit and a data display unit;

[0151] A data transmission unit, used to transmit the location information to the central node of the fusion command system through an optimized data transmission protocol;

[0152] The data display unit is used to display the location information in real time on the system interface, and to perform visual display of command, dispatch and resource allocation based on the location information.

[0153] Through the division of data acquisition module, correction module, position information calculation module, data transmission module, optimization module and their units, the improved position feedback algorithm optimization system of the fusion command system in the present invention can efficiently process position data, improve positioning accuracy, and realize the intelligence and visualization of command dispatch and resource allocation.

[0154] Example 3

[0155] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, steps of a method for optimizing position feedback of a fusion command system are implemented. For details, please refer to the above method embodiment, which will not be repeated here.

[0156] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. A method for optimizing the position feedback of a fusion command system, characterized in that: include: Step S1: collecting the location data of mobile personnel or vehicles from the fusion command system and preprocessing them; Step S2: using a linear discriminant analysis method to extract features from the preprocessed position data, obtain position feature data, and correct the position feature data; Step S3: using the improved position return algorithm to calculate the corrected position feature data to obtain position information, and associating the obtained position information with the audio and video data and GIS map in the fusion command system; Step S4: The calculated location information is transmitted to the central node of the fusion command system through the optimized data transmission protocol. At the same time, the location information is displayed on the interface of the fusion command system, and command dispatch and resource allocation are performed according to the location information. Step S5: Collecting user feedback and opinions on the improved location return algorithm, optimizing the improved location return algorithm according to the feedback, obtaining an optimized location return algorithm, redeploying the optimized location return algorithm into the fusion command system, and performing testing and verification; The specific steps of step S3 include: S3.1: Obtain corrected position feature data ,based on , using an improved location return algorithm Calculate and output the calculated position information, where: Indicates n Corrected position feature data, n Indicates the number of corrected position feature data; S3.2: Obtain audio and video data from the fusion command system, and associate the audio and video data with the calculated location information based on the timestamp and device ID to obtain associated location information; S3.3: Load GIS map data, map the associated location information onto the GIS map, determine the specific location, and display the associated audio and video data and location information; The specific steps of the improved position feedback algorithm in S3.1 include: S3.11: Obtain corrected position feature data , and initialize the initial state of the Kalman filter and the covariance matrix ,in, and represents the initial position, and represents the initial velocity; S3.12: At each time step k , using the state prediction equation and the covariance prediction equation To make a prediction, the formula is: ； in, Indicates k The state vector predicted before the step, represents the state transfer matrix, represents the posterior estimate of the previous moment, represents the control input matrix, represents the control input vector, represents the environmental factor matrix, represents the external interference vector; S3.13: Obtaining real-time corrected position feature data , and is updated using the Kalman gain equation, state update equation, and covariance update equation, as follows: ； in, represents the posterior estimate at the current moment, represents the Kalman gain, represents the observation matrix, represents the state correction matrix; S3.14: Output posterior estimates The location part and As the calculated position information, and Indicates the calculated speed information.

2. A method for optimizing the position feedback of a fusion command system as claimed in claim 1, characterized in that: The specific steps of step S2 include: S2.1: Obtaining preprocessed position data ,in, Indicates a Mobile personnel location data, Indicates b Vehicle location data, a Indicates the number of mobile personnel position data, b Indicates the quantity of vehicle location data; S2.2: Compute human position data and vehicle location data The mean vector of ,in, c =1, represents the mean vector of the mobile personnel position data, and , express The i location data, c =2, represents the mean vector of vehicle position data, and , express The i location data, c Indicates the category of location data, Indicates category c The number of location data.

3. A method for optimizing the position feedback of a fusion command system as claimed in claim 2, characterized in that: The specific steps of step S2 also include: S2.3: Calculate the intra-class scatter matrix and the between-class scatter matrix , the formula is: ； in, Represents a location data point In category c The weights in , T represents transpose, represents the regularization coefficient, Indicates category c The regularization matrix of Indicates category c The global weight of represents the mean of the preprocessed position data X; S2.4: By maximizing the between-class scatter matrix and the within-class scatter matrix The ratio of Solving the projection matrix ,and ,in represents the feature vector, Indicates d The transpose of the eigenvectors, d represents the number of eigenvectors, Represents the eigenvalue.

4. A method for optimizing the position feedback of a fusion command system as claimed in claim 3, characterized in that: The specific steps of step S2 also include: S2.5: Use the projection matrix to project the preprocessed position data into a low-dimensional space to obtain position feature data; S2.6: Load high-precision map data, and calculate the position of the current position feature data in the high-precision map according to the position feature data; S2.7: Match the location feature data with the road and intersection structured data in the high-precision map, and calculate the error between the location feature data and the high-precision map based on the matching result; S2.8: According to the error result, the projected position feature data is corrected using a Kalman filter algorithm to obtain corrected position feature data.

5. A position feedback optimization system of a fusion command system, which is used to implement a position feedback optimization method of a fusion command system according to any one of claims 1 to 4, characterized in that: include: Data acquisition module, correction module, position information calculation module, data transmission module, optimization module; The data acquisition module is used to collect the location data of mobile personnel or vehicles from the fusion command system and perform preliminary processing and cleaning; The correction module is used to correct the extracted location feature data using a map matching algorithm; The position information calculation module is used to calculate the corrected position feature data using an improved position return algorithm to obtain accurate position information, and associate the position information with the audio and video data and GIS map in the fusion command system; The data transmission module is used to transmit the location information to the central node of the integrated command system through the optimized data transmission protocol, and display the location information in real time on the system interface, and perform command dispatch and resource allocation according to the location information; The optimization module is used to collect user feedback and opinions on the improved location return algorithm, optimize the algorithm based on the feedback, and redeploy the optimized algorithm into the system for testing and verification.

6. A position feedback optimization system for integrating command systems as claimed in claim 5, characterized in that: The correction module includes: a feature extraction unit and a correction unit; The feature extraction unit is used to extract features from the preprocessed position data using a linear discriminant analysis method; The correction unit is used to correct the extracted position feature data using a map matching algorithm to reduce positioning errors.

7. A position feedback optimization system for integrating command systems as claimed in claim 6, characterized in that: The location information calculation module includes: a location calculation unit and a data association unit; The position calculation unit is used to calculate the corrected position feature data using an improved position feedback algorithm to obtain position information; The data association unit is used to associate the calculated location information with the audio and video data and the GIS map to form complete command and dispatch information.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for optimizing the position feedback of a fusion command system as described in any one of claims 1 to 4 are implemented.

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