Method and device for optimizing primary and secondary layout configuration of monitoring and early warning device of electrochemical energy storage station
By optimizing the location and coverage of sensor deployment points in electrochemical energy storage stations and adopting a satellite sensor model, the problem of sensor layout being unable to meet the monitoring needs of key locations has been solved, enabling effective response to different levels of risk and cost-effective monitoring coverage.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA COAL RES INST
- Filing Date
- 2025-11-21
- Publication Date
- 2026-04-28
AI Technical Summary
The existing sensor layout of energy storage stations is insufficient to meet the needs of effective monitoring of critical locations. The response speed is delayed and space and cost constraints limit large-scale deployment, increasing the risks and challenges to the timeliness of monitoring.
By adopting a satellite sensor deployment pattern, optimizing the location and coverage of sensor deployment points, and combining Markov chains and progressive coverage models, the number and type of sensors are configured to achieve the optimal monitoring coverage level.
It improves the safety and monitoring timeliness of electrochemical energy storage stations, reduces construction costs, and enables effective response to risks of different levels.
Smart Images
Figure CN121936078A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of energy storage monitoring technology, and in particular to a method and device for optimizing the main and auxiliary layout configuration of an electrochemical energy storage station monitoring and early warning device. Background Technology
[0002] The deployment of sensors at energy storage stations should ensure that monitoring data can be transmitted to the command center within a short time after a risk event, enabling emergency response. However, with the rapid growth of urban economies and electricity demand, the distribution of energy storage stations and their surrounding environments have changed significantly. In some cities, existing sensor deployments are insufficient for effective monitoring of critical locations, and sensor response speeds may be delayed due to network congestion or signal interference. Furthermore, limited space for sensor deployment, coupled with high costs and budget constraints, makes large-scale deployment of standard sensors difficult. These issues not only increase the severity of risks associated with energy storage stations but also pose greater challenges to the timeliness of monitoring and early warning systems. Summary of the Invention
[0003] This application aims to at least partially address one of the technical problems in the related art.
[0004] Therefore, the first objective of this application is to propose an optimization method for the main and auxiliary deployment of monitoring and early warning devices for electrochemical energy storage stations.
[0005] The second objective of this application is to provide an apparatus.
[0006] The third objective of this application is to propose an electronic device.
[0007] The fourth objective of this application is to provide a computer-readable storage medium.
[0008] The fifth objective of this application is to provide a computer program product.
[0009] To achieve the above objectives, the first aspect of this application proposes a method for optimizing the main and auxiliary deployment of monitoring and early warning devices for electrochemical energy storage stations, including: Identify the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each of the electrochemical risk points; Determine the deployment points for arranging sensors, including first type deployment points and second type deployment points; Select the second type of deployment point as the secondary deployment point of the first type of deployment point; The monitoring coverage level of electrochemical risk points is optimized based on the deployment points to determine the configuration of secondary deployment points and the number of sensors in each deployment point.
[0010] Optionally, determining the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point includes: Determine the total One electrochemical risk point, Risk level, The risk level is l The set of risk points; As a binary variable, when the demand point When covered, Otherwise, it is 0; let it be 0. Let i be the weight of the demand point. Indicates the upper limit of the distance between the demand point and the deployment point under the coverage condition; let This represents the set of deployment points that can cover demand point i, i.e. ,in Represents the shortest distance from demand point i to deployment point j; the asymptotic loss function for monitoring service levels. ,in yes Decreasing function set up To determine the capacity of deployment point j, the number of sensors deployed within energy storage station j is: The number of sensors dispatched from deployment point j to demand point i is ; and All are integer variables.
[0011] Optionally, determining the deployment points for arranging the sensors includes: The candidate sets for the first type of deployment points and the second type of deployment points are respectively and .make Let be a binary decision variable, representing the second type of deployment point. Can it be upgraded to a Type I deployment point? Satellite secondary deployment points; if and If a satellite relationship exists, then ;otherwise ; make This is a binary variable representing whether to construct a deployment point at the corresponding candidate point. For any first-type deployment point... It has the capability to monitor risks at all levels; For any second type of deployment point It can only monitor small-scale risk events. If and only if it is upgraded to a certain type 1 deployment point satellite secondary deployment sites (i.e. Only when [the situation is such that] they have the ability to monitor medium-level risks ( ). However, it lacks the ability to monitor high-level risks. ).
[0012] Optionally, selecting a second type of deployment point as a secondary deployment point for the first type of deployment point includes: set up This represents the upper limit of the distance between the first type of deployment point and the secondary deployment point under satellite association conditions. This indicates that it can be associated with the first type of deployment point. The set of secondary deployment points, i.e. Similarly, This indicates that it can be associated with secondary deployment points. The first type of deployment point set, namely ; in, This indicates the shortest travel distance from the secondary deployment point to the first type of deployment point; The cost associated with the first type of deployment point and secondary deployment point increases with distance as the function increases. Assuming If the unit related cost is , then the asymptotic related cost is . ,set up Let M be the construction cost of deployment point j, and M be the total budget used for the construction of deployment point j.
[0013] Optionally, optimizing the monitoring coverage level of electrochemical risk points based on the deployment points to determine the configuration of secondary deployment points and the number of sensors at each deployment point includes: Determine the monitoring coverage level for electrochemical energy storage: (1) The coverage level of electrochemical energy storage monitoring resources can be expressed as: ,in a i,v The number of sensors of type v configured for risk point i under progressive coverage conditions. b i,v The number of sensors of type v configured for risk point i in the monitoring plan. The effective response rate under balanced conditions is... ; To optimize the monitoring coverage level a i,v and b i,v .
[0014] Optionally, the method further includes: Determine the equilibrium model of the sensor system under cooperative operation: (2) in, and These represent the current stage and other stages of the Markov chain, respectively. Indicates the system state is The probability of. This indicates the service rate of a sensor combination working in concert. For the system from the past stage At the current stage The Hamming distance, its value is and The number of service provider status differences. Indicates the uplink Hamming distance. This indicates the downlink Hamming distance.
[0015] The normalized equation for the equilibrium formula can be expressed as: (3) The failure probability of the electrochemical energy storage monitoring system can be calculated by describing its state. To ensure the monitoring service level, the system's effective response rate is controlled within a certain threshold. above. (4) In equation (4), Indicates queuing equilibrium The set of all risk points that can be effectively monitored under the current conditions.
[0016] In the equilibrium formula N This refers to the total number of sensors of all types deployed at all locations, which can be expressed as: (5) Risk points The types of equipment are Number of sensors It can be represented as: (6) At lower levels, and , Value ; At the intermediate level, and , Value ; At higher levels, and , Value .
[0017] This means that the number of sensors of type v required for risk point i in the monitoring plan should not be less than the number of sensors after optimization. This constraint can be expressed as: (7) The right side of the inequality represents the optimized configuration types. Number of sensors. At lower levels, and Its value is ; At intermediate level, and Its value is ; At higher levels and Its value is .
[0018] Optionally, the constraints on the progressive coverage level of the electrochemical energy storage monitoring resources include: The number of sensors responding from a candidate deployment point should not exceed the total number of sensors configured at that deployment point. This constraint can be expressed as: (8) If at the candidate point When deploying sensors, a deployment point must be established at that location, and the total number of sensors deployed must not exceed the capacity of the deployment point. This constraint can be expressed as follows: (9) The total cost of site construction and related costs should be controlled within the fiscal budget, i.e. (10) In equation (10), For sensor deployment costs; Cost of inter-sensor correlation. For each small deployment point, the number of Type I deployment points it relies on is no more than one, that is... (11) The number of secondary deployment points that establish satellite relationships with Type 1 deployment points shall not exceed the upper limit of the number of secondary deployment points of the satellite deployment point. This constraint can be expressed as: (12) The prerequisite for the existence of satellite relationships is that both the primary deployment sites and the secondary deployment sites have been completed, that is, when Only then can a satellite relationship exist between the two. This constraint can be expressed as: (13) The following constraints specify the types of each decision variable: (14) (15) (16) (17) Optionally, the method further includes: Based on the optimized result a i,v and b i,v To determine the first type of deployment point and the corresponding secondary deployment point, and to arrange the corresponding number of sensors in the first type of deployment point and the corresponding secondary deployment point.
[0019] To achieve the above objectives, a second aspect of this application provides a main and auxiliary configuration optimization device for monitoring and early warning systems in electrochemical energy storage stations, comprising: The risk determination module is used to determine the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point. The deployment point determination module is used to determine the deployment points for arranging sensors, wherein the deployment points include a first type of deployment point and a second type of deployment point; The secondary deployment point filtering module is used to select second-type deployment points as secondary deployment points of first-type deployment points; The configuration module is used to optimize the monitoring coverage level of electrochemical risk points based on the deployment points to determine the configuration of sub-deployment points and the number of sensors in each deployment point.
[0020] To achieve the above objectives, a third aspect of this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.
[0021] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of the first aspects.
[0022] To achieve the above objectives, a fifth aspect of this application provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.
[0023] The main and auxiliary deployment optimization method, device, electronic equipment and storage medium of the electrochemical energy storage station monitoring and early warning device provided in this application achieve comprehensive monitoring of the electrochemical energy storage station by selecting the second type of deployment point as the auxiliary deployment point of the first type of deployment point, thereby improving the safety of the electrochemical energy storage station.
[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a method for optimizing the main and auxiliary deployment of an electrochemical energy storage station monitoring and early warning device, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of the main and auxiliary layout configuration optimization device for an electrochemical energy storage station monitoring and early warning device provided in an embodiment of this application. Detailed Implementation
[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0027] The deployment of sensors at energy storage stations should ensure that monitoring data can be transmitted to the command center within a short time after a risk event, enabling emergency response. However, with the rapid growth of urban economies and electricity demand, the distribution of energy storage stations and their surrounding environments have changed significantly. In some cities, existing sensor deployments are insufficient for effective monitoring of critical locations, and sensor response speeds may be delayed due to network congestion or signal interference. Furthermore, limited space for sensor deployment, coupled with high costs and budget constraints, makes large-scale deployment of standard sensors difficult. These issues not only increase the severity of risks associated with energy storage stations but also pose greater challenges to the timeliness of monitoring and early warning systems.
[0028] To address these challenges, some cities have begun constructing satellite sensor deployment sites, resolving many issues inherent in traditional deployment methods. Satellite deployment sites are small monitoring points established by building upon existing standard deployment sites and radiating outwards to surrounding areas. Compared to standard deployment sites, satellite deployment sites, with their smaller sensor capacity, are primarily used for monitoring small to medium-sized risks, but their construction costs are low, thus supplementing the coverage function of standard deployment sites. This satellite deployment model, based on the actual needs of energy storage station monitoring in my country and successful international experience, is expected to become a major trend in energy storage station monitoring planning.
[0029] Although there have been many studies on monitoring coverage models both domestically and internationally, there is still a lack of in-depth research on the optimization of satellite deployment sites under the risk monitoring mechanism of electrochemical energy storage stations in my country. This patent addresses the issue of satellite deployment site selection and coverage by considering the constraints of the main deployment site on the number, distance, and associated costs of satellite deployment sites; and optimizes the allocation of monitoring resources with different functions and costs based on the distribution of electrochemical risk points of different levels, in order to determine which small deployment sites should be upgraded to satellite deployment sites and rationally configure the sensors in the station to achieve the optimal monitoring coverage level with the lowest financial investment. In response to the shortcomings of current research, a capacity-constrained sensor deployment configuration optimization model under satellite cooperation conditions is constructed, which solves the following problems: (1) a collaborative response scheme for satellite deployment point sensors is formed; (2) an asymptotic decay function for the cooperation level between satellite deployment points is proposed; (3) a risk point service level objective function considering satellite cooperation relationship is constructed. From the perspective of engineering practice, scientifically constructing a satellite deployment site selection and coverage model can provide more effective decision support for the monitoring planning and resource optimization of large and medium-sized electrochemical energy storage stations.
[0030] Based on the electrochemical risk distribution results, a mixed-integer programming model for incremental coverage and sensor configuration under equilibrium conditions is proposed. The model considers coverage reliability under heavy facility loads and the mutual constraints between associated deployment points of different sizes (e.g., satellite deployment points and main deployment points), and performs incremental coverage optimization for sensor site selection and configuration schemes for different levels of electrochemical risk. By programming on the GAMS platform and combining a stochastic search algorithm and the steepest descent method for solution, the model is applied to the optimization of energy storage station monitoring point layout. Calculation results show that the satellite deployment point pattern has significant advantages in coverage level, and sensitivity analysis explores key parameters such as the associated costs of satellite deployment points, providing a useful reference for energy storage monitoring planning.
[0031] This application provides an embodiment of a method for optimizing the main and auxiliary deployment configuration of monitoring and early warning devices for electrochemical energy storage stations. Figure 1 This is a flowchart illustrating a method for optimizing the main and auxiliary deployment of an electrochemical energy storage station monitoring and early warning device, as provided in an embodiment of this application. Figure 1As shown, the method includes the following steps: Step 101: Determine the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point; Step 102: Determine the deployment points for arranging the sensors, including first type deployment points and second type deployment points; Step 103: Select the second type of deployment point as the secondary deployment point of the first type of deployment point; Step 104: Optimize the monitoring coverage level of electrochemical risk points based on the deployment points to determine the configuration of secondary deployment points and the number of sensors in each deployment point.
[0032] In this embodiment, based on the scale of the energy storage station deployment points, they can be divided into two types: Type I deployment points and Type II deployment points. Type I deployment points are standard deployment points, capable of accommodating 6 sensors. Type II deployment points are small deployment points, capable of accommodating 2-3 sensors. Since Type II deployment points have a limited number of sensors, they may not be able to meet the monitoring needs of high-risk areas, thus requiring support from other surrounding Type I deployment points. The satellite energy storage monitoring system primarily uses Type I deployment points, with Type II deployment points serving as auxiliary points, forming a cooperative relationship between the main deployment points and the satellite deployment points. Since the relationship between the main deployment points and the satellite deployment points is similar to that between a planet and its satellite, the Type I and Type II deployment points in the satellite energy storage monitoring system can be referred to as the main deployment point and the auxiliary deployment point, respectively.
[0033] Type I deployment sites are the mainstay of electrochemical risk monitoring systems, playing a decisive role. Whether Type II deployment sites can be set up in a certain area requires research and demonstration, taking into account factors such as the risk level of the area and sensor response time, to ensure that Type II deployment sites can effectively improve monitoring efficiency and meet practical needs.
[0034] Optionally, determining the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point includes: Determine the total One electrochemical risk point, Risk level, The risk level is l The set of risk points; As a binary variable, when the demand point When covered, Otherwise, it is 0; let it be 0. Let i be the weight of the demand point. Indicates the upper limit of the distance between the demand point and the deployment point under the coverage condition; let This represents the set of deployment points that can cover demand point i, i.e. ,in This represents the shortest distance from demand point i to deployment point j; Because electrochemical risk losses gradually increase with response distance, while monitoring service levels exhibit a gradual decline, the model considers the asymptotic coverage characteristics of demand points at different risk levels and introduces an asymptotic loss function for monitoring service levels. ,in yes A decreasing function.
[0035] The number of sensors deployed at urban energy storage stations is a major factor in determining their construction scale. To determine the capacity of deployment point j, the number of sensors deployed within energy storage station j is: The number of sensors dispatched from deployment point j to demand point i is ; and All are integer variables.
[0036] Optionally, determining the deployment points for arranging the sensors includes: The candidate sets for the first type of deployment points and the second type of deployment points are respectively and .make Let be a binary decision variable, representing the second type of deployment point. Can it be upgraded to a Type I deployment point? Satellite secondary deployment points; if and If a satellite relationship exists, then ;otherwise ; make This is a binary variable representing whether to construct a deployment point at the corresponding candidate point. For any first-type deployment point... It has the capability to monitor risks at all levels; For any second type of deployment point It can only monitor small-scale risk events. If and only if it is upgraded to a certain type 1 deployment point satellite secondary deployment sites (i.e. Only when [the situation is such that] they have the ability to monitor medium-level risks ( ). However, it lacks the ability to monitor high-level risks. ).
[0037] Based on spatial queuing theory and Markov processes, the effectiveness of emergency monitoring systems and the preset sensor response states can be effectively described. This refers to the reliability of the coverage of the demand points.
[0038] Optionally, selecting a second type of deployment point as a secondary deployment point for the first type of deployment point includes: set up This represents the upper limit of the distance between the first type of deployment point and the secondary deployment point under satellite association conditions. This indicates that it can be associated with the first type of deployment point. The set of secondary deployment points, i.e. Similarly, This indicates that it can be associated with secondary deployment points. The first type of deployment point set, namely ; in, This indicates the shortest travel distance from the secondary deployment point to the first type of deployment point; As the distance between the primary and secondary deployment points increases, the difficulty of ensuring the secondary deployment point's support becomes more challenging, leading to increased investment in communication and consequently higher collaboration costs. Therefore, the model considers the asymptotic association characteristic and incorporates an increasing function for the association cost between the first type of deployment point and the secondary deployment point as distance increases. Assuming If the unit related cost is , then the asymptotic related cost is . ,set up Let M be the construction cost of deployment point j, and M be the total budget used for the construction of deployment point j.
[0039] Optionally, optimizing the monitoring coverage level of electrochemical risk points based on the deployment points to determine the configuration of secondary deployment points and the number of sensors at each deployment point includes: Determine the monitoring coverage level for electrochemical energy storage: (1) The coverage level of electrochemical energy storage monitoring resources can be expressed as: ,in a i,v The number of sensors of type v configured for risk point i under progressive coverage conditions. b i,v The number of sensors of type v configured for risk point i in the monitoring plan. The effective response rate under balanced conditions is... ; To optimize the monitoring coverage level a i,v and b i,v .
[0040] Optionally, the method further includes: Determine the equilibrium model of the sensor system under cooperative operation: (2) in, and These represent the current stage and other stages of the Markov chain, respectively. Indicates the system state is The probability of. This indicates the service rate of a sensor combination working in concert. For the system from the past stage At the current stage The Hamming distance, its value is and The number of service provider status differences. Indicates the uplink Hamming distance. This indicates the downlink Hamming distance.
[0041] The normalized equation for the equilibrium formula can be expressed as: (3) The failure probability of the electrochemical energy storage monitoring system can be calculated by describing its state. To ensure the monitoring service level, the system's effective response rate is controlled within a certain threshold. above. (4) In equation (4), Indicates queuing equilibrium The set of all risk points that can be effectively monitored under the current conditions.
[0042] In the equilibrium formula N This refers to the total number of sensors of all types deployed at all locations, which can be expressed as: (5) Risk points The types of equipment are Number of sensors It can be represented as: (6) At lower levels, and , Value ; At the intermediate level, and , Value ; At higher levels, and , Value .
[0043] This means that the number of sensors of type v required for risk point i in the monitoring plan should not be less than the number of sensors after optimization. This constraint can be expressed as: (7) The right side of the inequality represents the optimized configuration types. Number of sensors. At lower levels, and Its value is ; At intermediate level, and Its value is ; At higher levels and Its value is .
[0044] Optionally, the constraints on the progressive coverage level of the electrochemical energy storage monitoring resources include: The number of sensors responding from a candidate deployment point should not exceed the total number of sensors configured at that deployment point. This constraint can be expressed as: (8) If at the candidate point When deploying sensors, a deployment point must be established at that location, and the total number of sensors deployed must not exceed the capacity of the deployment point. This constraint can be expressed as follows: (9) The total cost of site construction and related costs should be controlled within the fiscal budget, i.e. (10) In equation (10), For sensor deployment costs; Cost of inter-sensor correlation. For each small deployment point, the number of Type I deployment points it relies on is no more than one, that is... (11) The number of secondary deployment points that establish satellite relationships with Type 1 deployment points shall not exceed the upper limit of the number of secondary deployment points of the satellite deployment point. This constraint can be expressed as: (12) The prerequisite for the existence of satellite relationships is that both the primary deployment sites and the secondary deployment sites have been completed, that is, when Only then can a satellite relationship exist between the two. This constraint can be expressed as: (13) The following constraints specify the types of each decision variable: (14) (15) (16) (17) Optionally, the method further includes: Based on the optimized result a i,v and b i,v To determine the first type of deployment point and the corresponding secondary deployment point, and to arrange the corresponding number of sensors in the first type of deployment point and the corresponding secondary deployment point.
[0045] In this embodiment, a hybrid heuristic algorithm combining random search and steepest descent is used to find the maximum value of (1).
[0046] In the initial stage of the random search algorithm, a set of possible placement locations for the electrochemical risk sensor is first randomly generated, and each proposed solution is tested to evaluate whether it meets the system's minimum effective response rate standard. Based on ensuring that the minimum response standard is met, a set of candidate locations is selected, while solutions with locations that are too close together are filtered out to avoid wasting resources and lay the foundation for subsequent optimization steps. This process provides initial conditions for the subsequent steepest descent method optimization.
[0047] Next, the selected candidate set is fed into a queuing-balanced sensor deployment and collaborative configuration optimization model for further processing. At this stage, the algorithm demonstrates two advantages: firstly, it can eliminate invalid deployment points with insufficient coverage; secondly, it can clearly identify the location areas of potential optimal deployment points.
[0048] Since the objective function has non-convex properties and may have multiple local optima, this phased processing strategy significantly reduces the difficulty of solving the problem while improving the reliability and efficiency of the results.
[0049] In the application of the steepest descent method, a Zig-Zag strategy is employed to ensure that the search direction in each iteration is perpendicular to the direction of the previous iteration, thereby avoiding getting trapped in local optima. Specifically, optimization is performed by taking the negative value of the objective function for the coverage level, i.e., determining the optimal search path along the direction in which the coverage level increases the fastest, in order to quickly approach the target solution.
[0050] In this method, the initial search position is randomly selected, and the distribution of the placement points is gradually adjusted based on the negative gradient direction. By continuously reducing the gradient, the search range of the solution is gradually narrowed. When the gradient tends to flatten and the error with the optimal solution reaches a preset tolerance range, the iteration stops, and the optimal placement scheme is finally determined. This method can effectively avoid getting trapped in local optima, while significantly improving optimization efficiency and accuracy.
[0051] To achieve the above embodiments, this application also proposes an optimization device for the main and auxiliary layout configuration of an electrochemical energy storage station monitoring and early warning device. Figure 2 This is a schematic diagram of the main and auxiliary layout configuration optimization device for an electrochemical energy storage station monitoring and early warning device provided in an embodiment of this application. Figure 2 As shown, the device includes: Risk determination module 210 is used to determine the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point; The deployment point determination module 220 is used to determine the deployment points for arranging sensors, the deployment points including a first type of deployment point and a second type of deployment point; The secondary deployment point screening module 230 is used to select the second type of deployment point as the secondary deployment point of the first type of deployment point; Configuration module 240 is used to optimize the monitoring coverage level of electrochemical risk points based on the deployment points to determine the configuration of sub-deployment points and the number of sensors in each deployment point.
[0052] To implement the above embodiments, this application also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.
[0053] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.
[0054] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.
[0055] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0056] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.
[0057] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.
[0058] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0059] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0060] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.
[0061] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
[0062] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0063] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0064] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0065] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.
Claims
1. A method for optimizing the main and auxiliary deployment of monitoring and early warning devices in an electrochemical energy storage station, characterized in that, Includes the following steps: Identify the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each of the electrochemical risk points; Determine the deployment points for arranging sensors, including first type deployment points and second type deployment points; Select the second type of deployment point as the secondary deployment point of the first type of deployment point; The monitoring coverage level of electrochemical risk points is optimized based on the deployment points to determine the configuration of secondary deployment points and the number of sensors in each deployment point.
2. The method according to claim 1, characterized in that, The determination of electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point includes: Determine the total One electrochemical risk point, Risk level, The risk level is l The set of risk points; As a binary variable, when the demand point When covered, Otherwise, it is 0; let it be 0. Let i be the weight of the demand point. Indicates the upper limit of the distance between the demand point and the deployment point under the coverage condition; let This represents the set of deployment points that can cover demand point i, i.e. ,in Represents the shortest distance from demand point i to deployment point j; the asymptotic loss function for monitoring service levels. ,in yes Decreasing function set up To determine the capacity of deployment point j, the number of sensors deployed within energy storage station j is: The number of sensors dispatched from deployment point j to demand point i is ; and All are integer variables.
3. The method according to claim 2, characterized in that, The determination of the deployment points for arranging the sensors includes: The candidate sets for the first type of deployment points and the second type of deployment points are respectively and ;make Let be a binary decision variable, representing the second type of deployment point. Can it be upgraded to a Type I deployment point? Satellite secondary deployment points; if and If a satellite relationship exists, then ;otherwise ; make This is a binary variable, representing whether to construct a deployment point at the corresponding candidate point; for any first-type deployment point... It has the capability to monitor risks at all levels; For any second type of deployment point It can only monitor small-scale risk events. If and only if it is upgraded to a certain type 1 deployment point satellite secondary deployment sites (i.e. Only when [the situation is such that] they have the ability to monitor medium-level risks ( ). However, it lacks the ability to monitor high-level risks. ).
4. The method according to claim 3, characterized in that, The selection of the second type of deployment point as the secondary deployment point of the first type of deployment point includes: set up This represents the upper limit of the distance between the first type of deployment point and the secondary deployment point under satellite association conditions. This indicates that it can be associated with the first type of deployment point. The set of secondary deployment points, i.e. Similarly, This indicates that it can be associated with secondary deployment points. The first type of deployment point set, namely ; in, This indicates the shortest travel distance from the secondary deployment point to the first type of deployment point; The cost associated with the first type of deployment point and secondary deployment point increases with distance as the function increases. Assuming If the unit related cost is , then the asymptotic related cost is . ,set up Let M be the construction cost of deployment point j, and M be the total budget used for the construction of deployment point j.
5. The method according to claim 4, characterized in that, The optimization of monitoring coverage of electrochemical risk points based on deployment points to determine the configuration of secondary deployment points and the number of sensors at each deployment point includes: Determine the monitoring coverage level for electrochemical energy storage: (1) The coverage level of electrochemical energy storage monitoring resources can be expressed as: ,in a i,v The number of sensors of type v configured for risk point i under progressive coverage conditions. b i,v The number of sensors of type v configured for risk point i in the monitoring plan; the effective response rate under balanced conditions is ; To optimize the monitoring coverage level a i,v and b i,v .
6. The method according to claim 5, characterized in that, The method further includes: Determine the equilibrium model of the sensor system under cooperative operation: (2) in, and These represent the current stage and other stages of the Markov chain, respectively; Indicates the system state is The probability of; This indicates the service rate of a sensor combination working in concert. For the system from the past stage At the current stage The Hamming distance, its value is and The number of service provider status differences; Indicates the uplink Hamming distance. Indicates the downlink Hamming distance; The normalized equation for the equilibrium formula can be expressed as: (3) (4) Indicates queuing equilibrium The set of all risk points that can be effectively monitored under the current conditions; In the equilibrium formula N This refers to the total number of sensors of all types deployed at all locations, which can be expressed as: (5) Risk points The types of equipment are Number of sensors It can be represented as: (6) At lower levels, and , Value ； At the intermediate level, and , Value ； At higher levels, and , Value ； This means that the number of sensors of type v required for risk point i in the monitoring plan should not be less than the number of sensors after optimization; this constraint can be expressed as: (7) (7) The right side of the greater than or equal to sign indicates the optimized configuration type. Number of sensors; At lower levels and Its value is ; At intermediate level, and Its value is ; At higher levels and Its value is .
7. The method according to claim 6, characterized in that, The constraints on the progressive coverage level of electrochemical energy storage monitoring resources include: The number of sensors responding from a candidate deployment point should not exceed the total number of sensors configured at that deployment point; this constraint can be expressed as: (8) If at the candidate point When deploying sensors, a deployment point must be established at that location, and the total number of sensors deployed must not exceed the capacity of the deployment point. This constraint can be expressed as follows: (9) The total cost of site construction and related costs should be controlled within the fiscal budget, i.e. (10) In equation (10), For sensor deployment costs; Cost of inter-sensor correlation; For each small deployment point, the number of Type I deployment points it relies on is no more than one, that is... (11) The number of secondary deployment points that establish satellite relationships with Type 1 deployment points shall not exceed the upper limit of the number of secondary deployment points of the satellite deployment point. This constraint can be expressed as: (12) The prerequisite for the existence of satellite relationships is that both the primary deployment sites and the secondary deployment sites have been completed, that is, when Only then can a satellite relationship exist between the two; this constraint can be expressed as: (13) The following constraints specify the types of each decision variable: (14) (15) (16) (17)。 8. The method according to claim 7, characterized in that, The method further includes: Based on the optimized result a i,v and b i,v To determine the first type of deployment point and the corresponding secondary deployment point, and to arrange the corresponding number of sensors in the first type of deployment point and the corresponding secondary deployment point.
9. A main and auxiliary deployment optimization device for monitoring and early warning systems in an electrochemical energy storage station, characterized in that, include: The risk determination module is used to determine the electrochemical risk points in the electrochemical energy storage station and the risk level corresponding to each electrochemical risk point. The deployment point determination module is used to determine the deployment points for arranging sensors, wherein the deployment points include a first type of deployment point and a second type of deployment point; The secondary deployment point filtering module is used to select second-type deployment points as secondary deployment points of first-type deployment points; The configuration module is used to optimize the monitoring coverage level of electrochemical risk points based on the deployment points to determine the configuration of sub-deployment points and the number of sensors in each deployment point.
10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.