Intelligent networked automobile safe driving decision control system based on mixed traffic environment

Abstract

The invention belongs to the field of automobile safe driving, relates to a data analysis technology, and aims to solve the problem that in the prior art, different types of environment information cannot be independently analyzed and then an optimal control decision cannot be generated, in particular to an intelligent network connection automobile safe driving decision control system based on a mixed traffic environment. Comprising a sensing module, a decision planning module and a control execution module which are connected in sequence, and the sensing module comprises a static sensing unit and a dynamic sensing unit; the road condition basic information and the dynamic environment information of the intelligent network connection automobile are sent to a decision planning module; according to the invention, the road condition basic information and the dynamic environment information of the intelligent network connection automobile can be respectively collected, then the decision planning module generates corresponding control instructions according to the road condition basic information and the dynamic environment information, and the final safety instruction is marked according to the safety priority of the control instructions. And driving safety decision analysis is carried out in a comprehensive complex mixed traffic environment.

Description

Technical Field

[0001] The present invention belongs to the field of automotive safe driving, relates to data analysis technology, and specifically is an intelligent connected vehicle safe driving decision control system based on a mixed traffic environment. Background Art

[0002] Intelligent connected vehicles achieve real-time information interaction in scenarios such as vehicle-to-vehicle and vehicle-to-road by integrating in-vehicle sensors, communication networks, and artificial intelligence technologies, supporting environmental perception, collaborative decision-making, and intelligent control, thereby enhancing driving safety and traffic efficiency.

[0003] The invention patent with the publication number CN116777062A discloses an adaptive fusion learning autonomous driving safety decision-making method for extreme difficult cases. This method realizes safe and intelligent response to extreme difficult cases through steps such as real-time perception and extreme difficult case scenario data collection, data preprocessing and feature classification extraction, model training and optimization, decision generation, and real-time monitoring and feedback. It can analyze and predict the possibilities and risks of various extreme difficult cases and generate and optimize safety decisions. However, there are many influencing factors in the automotive driving environment, including relatively static trajectory road condition information and dynamic distance information between vehicles. The existing technology cannot independently analyze different types of environmental information and then generate an optimal control decision, resulting in the inability to screen and optimize abnormal links in a timely and effective manner when there are risk hazards in the control decision.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent connected vehicle safe driving decision control system based on a mixed traffic environment to solve the problem that the existing technology cannot independently analyze different types of environmental information and then generate an optimal control decision. The technical problem that the present invention needs to solve is: how to provide an intelligent connected vehicle safe driving decision control system based on a mixed traffic environment that can independently analyze different types of environmental information and then generate an optimal control decision.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An intelligent connected vehicle safe driving decision control system based on a mixed traffic environment includes a perception module, a decision-making and planning module, and a control execution module that are connected in sequence. The perception module includes a static perception unit and a dynamic perception unit. The static perception unit obtains the position and navigation route of the intelligent connected vehicle in real time, uploads the position and navigation route of the intelligent connected vehicle to the vehicle network, and retrieves the basic road condition information within the nearest L1 meters of the driving route of the intelligent connected vehicle through the vehicle network, and sends the basic road condition information of the intelligent connected vehicle to the decision-making and planning module; The dynamic perception unit obtains the dynamic environment information of the intelligent connected vehicle in real time and sends the dynamic environment information of the intelligent connected vehicle to the decision-making and planning module; The decision-making and planning module retrieves the driving decision instruction sequence of the intelligent connected vehicle; compares the basic road condition information, dynamic environment information with the driving decision instruction sequence respectively to obtain the static control instruction and the dynamic control instruction, marks the sequence numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence as the static evaluation value and the dynamic evaluation value respectively, and marks the safety instruction through the static evaluation value and the dynamic evaluation value; sends the safety instruction to the control execution module; The decision-making and planning module is also communicatively connected to a safety evaluation platform. After generating the safety instruction, the decision-making and planning module synchronously sends the safety instruction to the safety evaluation platform, and the safety evaluation platform is communicatively connected to a safety evaluation module and a decision optimization analysis module.

[0007] Further, the driving decision instruction sequence is a sequence obtained by sorting a number of driving instructions in ascending order of safety level. The driving instructions include stopping, braking, decelerating, constant speed, lane changing, and overtaking; and the driving instructions correspond to the basic road condition information and the dynamic environment information.

[0008] Further, the marking process of the safety instruction includes: comparing the static evaluation value with the dynamic evaluation value: if the static evaluation value is less than the dynamic evaluation value, mark the static control instruction as the safety instruction; if the static evaluation value is greater than or equal to the dynamic evaluation value, mark the dynamic control instruction as the safety instruction.

[0009] Further, the safety evaluation module is used to evaluate and analyze the driving decision safety of the intelligent connected vehicle: mark the intelligent connected vehicle connected to the safety evaluation platform as the evaluation object, generate an evaluation period and divide the evaluation period into several evaluation time periods, extract and mark the switching process of the safety instruction from the high-sequence state to the low-sequence state of the evaluation object during the evaluation time period as the evaluation process, and mark the evaluation process as a risk process or a safety process; mark the ratio of the number of risk processes marked during the evaluation time period to the number of evaluation processes as the risk coefficient of the evaluation time period, and determine whether the driving decision safety of the evaluation object meets the requirements during the evaluation time period through the risk coefficient.

[0010] Further, the specific process of marking the evaluation process as a risk process or a safety process includes: determining whether the evaluation process conforms to the risk characteristics: if so, marking the evaluation process as a risk process; if not, marking the evaluation process as a safety process; the risk characteristics include that the duration of the evaluation process is less than M1 seconds and the difference between the high serial number and the low serial number is not less than two.

[0011] Further, the specific process of determining whether the driving decision safety of the evaluation object meets the requirements during the evaluation period includes: comparing the risk coefficient with a preset risk threshold: if the risk coefficient is less than the risk threshold, it is determined that the driving decision safety of the evaluation object meets the requirements during the evaluation period; if the risk coefficient is greater than or equal to the risk threshold, it is determined that the driving decision safety of the evaluation object does not meet the requirements during the evaluation period, a decision optimization analysis signal is generated and the decision optimization analysis signal is sent to the decision optimization analysis module through the safety evaluation platform.

[0012] Further, the decision optimization analysis module is used to optimize and analyze the safe driving decision-making process of the intelligent connected vehicle: mark the serial numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence corresponding to the pre-safe instruction of the risk process as J-1 and D-1 respectively, and mark the serial numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence corresponding to the post-safe instruction of the risk process as J-2 and D-2 respectively; by marking the difference between J-2 and J-1 and the difference between D-2 and D-1 as the static optimization value and the dynamic optimization value respectively, compare the static optimization value and the dynamic optimization value and generate an optimization signal according to the comparison result.

[0013] Further, the specific process of comparing the static optimization value and the dynamic optimization value includes: if the static optimization value is less than the dynamic optimization value, generate a dynamic optimization signal and send the dynamic optimization signal to the mobile terminal of the management personnel through the safety evaluation platform; if the static optimization value is greater than the dynamic optimization value, generate a static optimization signal and send the static optimization signal to the mobile terminal of the management personnel through the safety evaluation platform; if the static optimization value is equal to the dynamic optimization value, generate a two-way optimization signal and send the two-way optimization signal to the mobile terminal of the management personnel.

[0014] The present invention has the following beneficial effects: Through the perception module, the basic road condition information and dynamic environment information of the intelligent connected vehicle can be collected respectively, and then the decision and planning module generates corresponding control instructions according to the basic road condition information and dynamic environment information, and marks the final safety instructions according to the safety priority of the control instructions. During this process, a driving safety decision analysis is carried out by integrating a complex mixed traffic environment, ensuring driving safety; The driving decision-making safety of the intelligent connected vehicle can be evaluated and analyzed through the safety assessment module. The risk coefficient is obtained by analyzing the risk degree of the safety instructions in each evaluation period in a periodic and time-segmented monitoring manner. The driving decision-making safety in the evaluation period is evaluated through the risk coefficient, and the optimization analysis is triggered in time when the safety is abnormal. The safety driving decision-making process of the intelligent connected vehicle can be optimized and analyzed through the decision optimization analysis module. The static optimization value and the dynamic optimization value are obtained by numerically analyzing the sequence numbers of the static control instructions and the dynamic control instructions in the driving decision instruction sequence during the risk process. The optimization measures for the safety driving decision are directly marked according to the static optimization value and the dynamic optimization value, improving the optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is the system block diagram of Embodiment 1 of the present invention; Figure 2 It is the system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] Embodiment 1: As Figure 1 - Figure 2 shown, the intelligent connected vehicle safety driving decision control system based on the mixed traffic environment includes a perception module, a decision-making and planning module, and a control execution module connected in sequence. The perception module includes a static perception unit and a dynamic perception unit; the perception module is communicatively connected to the vehicle network.

[0019] The static perception unit is used to perceive and analyze the static traffic environment during the driving process of the intelligent connected vehicle: obtain the position and navigation route of the intelligent connected vehicle in real time, upload the position and navigation route of the intelligent connected vehicle to the vehicle network, and retrieve the basic road condition information of the nearest L1 meters in the driving path of the intelligent connected vehicle through the vehicle network. The basic road condition information includes: sharp turn sections with a radius of curvature less than L2 meters, turning sections with a radius of curvature greater than or equal to L2 meters, landslide sections, tunnel sections, traffic flow in the driving path greater than L3, etc.; send the basic road condition information of the intelligent connected vehicle to the decision-making and planning module; it should be noted that different basic road condition information corresponds to different safety levels. For example, the safety level of "including landslide sections" is relatively low. When there are landslide sections, it can correspond to "brake" in the driving decision instruction sequence. For example, the safety level of "including tunnel sections" is relatively higher than that of landslide sections. When there are tunnel sections, it can correspond to "decelerate" in the driving decision instruction program. Here, L1, L2, and L3 are all numerical constants, and the specific values of L1, L2, and L3 are set by the management personnel. The dynamic perception unit is used to perceive and analyze the dynamic traffic environment during the driving process of the intelligent connected vehicle: obtain the dynamic environment information of the intelligent connected vehicle in real time. The dynamic environment information includes: the distance between the intelligent connected vehicle and the vehicle in front is less than K1 meters and the driving speed of the intelligent connected vehicle is not less than K2 kilometers per hour, the minimum distance between the intelligent connected vehicle and the nearest vehicle in the adjacent lanes on both sides is less than K3 meters, the rainfall during the driving of the intelligent connected vehicle is higher than K4 millimeters and the driving speed of the intelligent connected vehicle is not less than K5 kilometers per hour; send the dynamic environment information of the intelligent connected vehicle to the decision-making and planning module; similar to the basic road condition information, different information in the dynamic environment information also corresponds to different safety levels. For example, "whether the distance between the intelligent connected vehicle and the vehicle in front is less than K1 meters and the driving speed of the intelligent connected vehicle is not less than K2 kilometers per hour" indicates that the vehicle speed is relatively high and the distance from the vehicle in front is relatively close, and its safety level is extremely low. Therefore, when the discrimination result of this information is in line, it can correspond to "stop braking" in the driving decision instruction sequence; here, K1, K2, K3, K4, and K5 are all numerical constants, and the specific values of K1, K2, K3, K4, and K5 are set by the management personnel. Collect the basic road condition information and dynamic environment information of the intelligent connected vehicle respectively, and then generate corresponding control instructions through the decision-making and planning module according to the basic road condition information and dynamic environment information. Mark the final safety instructions according to the safety priority of the control instructions. In this process, a complex mixed traffic environment is comprehensively considered for driving safety decision-making analysis, ensuring driving safety.

[0020] The decision-making and planning module is used to plan and analyze the safe driving decisions of intelligent connected vehicles: retrieve the driving decision instruction sequence of the intelligent connected vehicle. The driving decision instruction sequence is a sequence obtained by sorting a number of driving instructions in ascending order of safety level. The driving instructions include stopping, braking, decelerating, maintaining a constant speed, changing lanes, and overtaking. Moreover, the driving instructions correspond to the basic road condition information and the dynamic environment information. Compare the basic road condition information with the driving decision instruction sequence to obtain a static control instruction, and compare the dynamic environment information with the driving decision instruction sequence to obtain a dynamic control instruction. Mark the sequence numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence as the static evaluation value and the dynamic evaluation value respectively, and compare the static evaluation value with the dynamic evaluation value: If the static evaluation value is less than the dynamic evaluation value, mark the static control instruction as a safety instruction; if the static evaluation value is greater than or equal to the dynamic evaluation value, mark the dynamic control instruction as a safety instruction. Send the safety instruction to the control execution module.

[0021] After receiving the safety instruction, the control execution module controls the intelligent connected vehicle according to the safety instruction.

[0022] Embodiment 2: The decision-making and planning module is also communicatively connected to a safety evaluation platform. After generating a safety instruction, the decision-making and planning module synchronously sends the safety instruction to the safety evaluation platform. The safety evaluation platform is communicatively connected to a safety evaluation module and a decision optimization and analysis module.

[0023] The safety assessment module is used to evaluate and analyze the driving decision safety of intelligent connected vehicles: Mark the intelligent connected vehicle connected to the safety assessment platform as the assessment object, generate an assessment period and divide the assessment period into several assessment time slots. Extract and mark the switching process of the safety instruction from the high serial number state to the low serial number state within the assessment time slot for the assessment object as the assessment process, and determine whether the assessment process conforms to the risk characteristics: If so, mark the assessment process as a risk process; if not, mark the assessment process as a safety process. The risk characteristics include that the duration of the assessment process is less than M1 seconds and the difference between the high serial number and the low serial number is not less than two. M1 is a numerical constant, and the specific value of M1 is set by the management personnel. Mark the ratio of the number of risk processes marked within the assessment time slot to the number of assessment processes as the risk coefficient of the assessment time slot, and compare the risk coefficient with the preset risk threshold: If the risk coefficient is less than the risk threshold, it is determined that the driving decision safety of the assessment object meets the requirements within the assessment time slot; if the risk coefficient is greater than or equal to the risk threshold, it is determined that the driving decision safety of the assessment object does not meet the requirements within the assessment time slot, generate a decision optimization analysis signal and send the decision optimization analysis signal to the decision optimization analysis module through the safety assessment platform. Analyze the risk degree of the safety instruction within each assessment time slot in a periodic sub-time slot monitoring manner to obtain the risk coefficient, and evaluate the driving decision safety within the assessment time slot through the risk coefficient, and trigger the optimization analysis in a timely manner when the safety is abnormal.

[0024] The decision optimization analysis module is used to optimize and analyze the safe driving decision-making process of intelligent connected vehicles: Mark the serial numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence corresponding to the previous safety instruction of the risk process as J-1 and D-1 respectively, and mark the serial numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence corresponding to the subsequent safety instruction of the risk process as J-2 and D-2 respectively. Mark the difference between J-2 and J-1 and the difference between D-2 and D-1 as the static optimization value and the dynamic optimization value respectively, and compare the static optimization value with the dynamic optimization value: If the static optimization value is less than the dynamic optimization value, generate a dynamic optimization signal and send the dynamic optimization signal to the mobile terminal of the management personnel through the safety assessment platform; if the static optimization value is greater than the dynamic optimization value, generate a static optimization signal and send the static optimization signal to the mobile terminal of the management personnel through the safety assessment platform; if the static optimization value is equal to the dynamic optimization value, generate a two-way optimization signal and send the two-way optimization signal to the mobile terminal of the management personnel. Optimize and analyze the safe driving decision-making process of intelligent connected vehicles, perform numerical analysis on the serial numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence during the risk process to obtain the static optimization value and the dynamic optimization value, and directly mark the optimization measures for the safe driving decision according to the static optimization value and the dynamic optimization value to improve the optimization efficiency.

[0025] Intelligent connected vehicle safety driving decision control system based on mixed traffic environment. During operation, it perceives and analyzes the static traffic environment during the driving process of the intelligent connected vehicle to obtain basic road condition information, perceives and analyzes the dynamic traffic environment during the driving process of the intelligent connected vehicle to obtain dynamic environment information, retrieves the driving decision instruction sequence of the intelligent connected vehicle, compares the basic road condition information with the driving decision instruction sequence to obtain a static control instruction, compares the dynamic environment information with the driving decision instruction sequence to obtain a dynamic control instruction, marks the serial numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence as the static evaluation value and the dynamic evaluation value respectively, and marks the safety instruction through the static evaluation value and the dynamic evaluation value; generates an evaluation period and divides the evaluation period into several evaluation time periods, extracts and marks the switching process of the safety instruction from the high serial number state to the low serial number state during the evaluation time period as the evaluation process, marks the evaluation process as a safe process or a risk process, marks the ratio of the number of risk processes marked during the evaluation time period to the number of evaluation processes as the risk coefficient of the evaluation time period, determines whether the driving decision safety of the evaluation object meets the requirements during the evaluation time period through the risk coefficient, and performs optimization analysis when the requirements are not met.

[0026] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all belong to the protection scope of the present invention.

[0027] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0028] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, according to the content of this specification, many modifications and changes can be made. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, so that those skilled in the art of this technology can understand and utilize the present invention well. The present invention is only limited by the claim book and its full scope and equivalents.

Claims

1. An intelligent connected vehicle safety driving decision control system based on a mixed traffic environment, comprising a perception module, a decision-making and planning module, and a control execution module that are connected in sequence, characterized in that, The perception module includes a static perception unit and a dynamic perception unit; The static perception unit obtains the position and navigation route of the intelligent connected vehicle in real time, uploads the position and navigation route of the intelligent connected vehicle to the vehicle network, and retrieves the basic road condition information of the nearest L1 meters in the driving path of the intelligent connected vehicle through the vehicle network, and sends the basic road condition information of the intelligent connected vehicle to the decision-making and planning module; The dynamic perception unit obtains the dynamic environment information of the intelligent connected vehicle in real time and sends the dynamic environment information of the intelligent connected vehicle to the decision-making and planning module; The decision-making and planning module retrieves the driving decision instruction sequence of the intelligent connected vehicle; compares the basic road condition information, dynamic environment information and driving decision instruction sequence respectively to obtain static control instructions and dynamic control instructions, marks the serial numbers of the static control instructions and dynamic control instructions in the driving decision instruction sequence as static evaluation values and dynamic evaluation values respectively, and marks the safety instructions through the static evaluation values and dynamic evaluation values; sends the safety instructions to the control execution module; The decision-making and planning module is also communicatively connected to a safety evaluation platform. After generating the safety instructions, the decision-making and planning module synchronously sends the safety instructions to the safety evaluation platform, and the safety evaluation platform is communicatively connected to a safety evaluation module and a decision optimization analysis module.

2. The intelligent networked vehicle safe driving decision control system based on a mixed traffic environment according to claim 1, characterized in that, The driving decision instruction sequence is a sequence obtained by sorting a number of driving instructions in ascending order of safety level. The driving instructions include stopping, braking, decelerating, constant speed, lane changing and overtaking; and the driving instructions correspond to the basic road condition information and dynamic environment information.

3. The intelligent networked vehicle safe driving decision control system based on a mixed traffic environment according to claim 2, wherein The marking process of the safety instructions includes: comparing the static evaluation value and the dynamic evaluation value: if the static evaluation value is less than the dynamic evaluation value, the static control instruction is marked as a safety instruction; if the static evaluation value is greater than or equal to the dynamic evaluation value, the dynamic control instruction is marked as a safety instruction.

4. The intelligent connected vehicle safe driving decision control system based on a mixed traffic environment according to claim 3, wherein, The safety evaluation module is used to evaluate and analyze the driving decision safety of the intelligent connected vehicle: mark the intelligent connected vehicle connected to the safety evaluation platform as the evaluation object, generate an evaluation period and divide the evaluation period into several evaluation time periods, extract and mark the switching process of the safety instructions from the high serial number state to the low serial number state of the evaluation object during the evaluation time period as the evaluation process, and mark the evaluation process as a risk process or a safety process; Mark the ratio of the number of risk processes marked during the evaluation time period to the number of evaluation processes as the risk coefficient of the evaluation time period, and determine whether the driving decision safety of the evaluation object meets the requirements during the evaluation time period through the risk coefficient.

5. The intelligent networked vehicle safe driving decision control system based on a mixed traffic environment according to claim 4, wherein The specific process of marking the evaluation process as a risk process or a safety process includes: determining whether the evaluation process meets the risk characteristics: if so, mark the evaluation process as a risk process; if not, mark the evaluation process as a safety process; the risk characteristics include that the duration of the evaluation process is less than M1 seconds and the difference between the high serial number and the low serial number is not less than two.

6. The intelligent connected vehicle safe driving decision control system based on a mixed traffic environment according to claim 5, wherein The specific process for determining whether the driving decision safety of the evaluation object meets the requirements during the evaluation period includes: comparing the risk coefficient with a preset risk threshold. If the risk coefficient is less than the risk threshold, it is determined that the driving decision safety of the evaluation object meets the requirements during the evaluation period. If the risk coefficient is greater than or equal to the risk threshold, it is determined that the driving decision safety of the evaluation object does not meet the requirements, a decision optimization analysis signal is generated, and the decision optimization analysis signal is sent to the decision optimization analysis module through the safety assessment platform.

7. The intelligent connected vehicle safe driving decision control system based on the mixed traffic environment according to claim 6, wherein, The decision optimization analysis module is used to optimize and analyze the safe driving decision-making process of the intelligent connected vehicle. The sequence numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence corresponding to the pre-risk safety instruction in the risk process are respectively marked as J-1 and D-1, and the sequence numbers of the static control instruction and the dynamic control instruction in the driving decision instruction sequence corresponding to the post-risk safety instruction in the risk process are respectively marked as J-2 and D-2. By respectively marking the difference between J-2 and J-1 and the difference between D-2 and D-1 as the static optimization value and the dynamic optimization value, the static optimization value and the dynamic optimization value are compared, and an optimization signal is generated based on the comparison result.

8. The intelligent networked vehicle safe driving decision control system based on a mixed traffic environment according to claim 7, wherein, The specific process of comparing the static optimization value and the dynamic optimization value includes: if the static optimization value is less than the dynamic optimization value, a dynamic optimization signal is generated and the dynamic optimization signal is sent to the mobile terminal of the management personnel through the safety assessment platform; if the static optimization value is greater than the dynamic optimization value, a static optimization signal is generated and the static optimization signal is sent to the mobile terminal of the management personnel through the safety assessment platform; if the static optimization value is equal to the dynamic optimization value, a two-way optimization signal is generated and the two-way optimization signal is sent to the mobile terminal of the management personnel.

Images