CN112541781B - Collaborative planning method and device for camera and edge node, electronic equipment and medium - Google Patents

Collaborative planning method and device for camera and edge node, electronic equipment and medium Download PDF

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CN112541781B
CN112541781B CN202011378867.2A CN202011378867A CN112541781B CN 112541781 B CN112541781 B CN 112541781B CN 202011378867 A CN202011378867 A CN 202011378867A CN 112541781 B CN112541781 B CN 112541781B
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邓浩然
郑文先
张阳
肖婷
黄映婷
刘佳斌
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Shenzhen Intellifusion Technologies Co Ltd
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Abstract

The embodiment of the invention provides a collaborative planning method, a collaborative planning device, electronic equipment and a medium for a camera and an edge node, wherein the method comprises the following steps: under the condition of ensuring that the safety operation constraint of the camera and the edge node, the stable operation constraint of data communication, the total data source constraint and the multipath constraint of the camera and the edge node are met, a multi-stage joint planning model taking the minimum investment cost and the minimum operation cost as objective functions is established; determining a collaborative planning scheme of the camera and the edge node according to the multi-stage joint planning model; the data source aggregate constraints include: data type constraints, yield constraints of data, and data origin constraints; the camera and edge node multipath constraints include: distance constraint of camera and edge node, number constraint of camera and edge node. The method can reasonably and cooperatively select the addresses of the cameras and the edge nodes, and provides equipment planning guidance for the data volume supply and demand balance required by the construction of the smart city under the condition of reducing the equipment investment cost and the operation cost.

Description

Collaborative planning method and device for camera and edge node, electronic equipment and medium
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, an electronic device, and a medium for collaborative planning of cameras and edge nodes.
Background
Along with the advanced development of artificial intelligence, more and more application scenes of artificial intelligence are landed, such as smart cities, smart trips and the like. The application scene of artificial intelligence is a data-driven application scene, and a large amount of data is required for driving and iterating, especially an application scene based on an image technology, and a large amount of image data is required. These massive image data are processed into structured data for storage, and then, as cameras are deployed more and more, the images to be processed are massive, and massive computing resources are also required, resulting in high computing cost. Due to the proposition of the IoT, the edge node is also applied to artificial intelligence, i.e., the AIoT of the artificial intelligence, specifically, communication between the camera and the edge node is established, the edge node is a device or platform with computing capability, the camera transmits the acquired image data to the edge node for relevant image processing, and the edge node transmits the image processing result to a local server or a cloud server for storage, so that massive image data do not need to be gathered together for processing. However, in the smart city landing process, the planning schemes of the cameras and the edge nodes only consider monitoring just needed, namely, vehicle snapshot cameras are deployed at traffic intersections, district entrance guard deployment personnel snapshot cameras and the like, so that some area cameras and edge nodes in the city are dense, and some areas are not provided with cameras, so that the acquired image data are relatively dense in source, the situation of uneven data distribution exists, and the whole iteration of the smart city is not facilitated.
Disclosure of Invention
The embodiment of the invention provides a collaborative planning method for cameras and edge nodes, which can reasonably and cooperatively select the addresses of the cameras and the edge nodes, and provides equipment planning guidance for data volume supply and demand balance required by smart city construction under the condition of reducing equipment investment cost and operation cost.
In a first aspect, an embodiment of the present invention provides a collaborative planning method for a camera and an edge node, where the method includes:
under the condition of ensuring that the safety operation constraint of the camera and the edge node, the stable operation constraint of data communication, the total data source constraint and the multipath constraint of the camera and the edge node are met, a multi-stage joint planning model taking the minimum investment cost and the minimum operation cost as objective functions is established;
determining a collaborative planning scheme of the camera and an edge node according to the multi-stage joint planning model;
the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node;
the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server;
The data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints;
the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node.
Optionally, the investment cost includes: camera equipment investment cost for camera type i, camera installation mating investment cost for camera type i, and investment equipment cost for edge nodes.
Optionally, the running cost includes: the method comprises the steps of camera operation maintenance cost with the camera type of i, camera installation matching maintenance cost with the camera type of i, communication cost with the communication mode of the camera and the edge node of j, communication cost with the communication mode of the edge node and the server of k and operation maintenance cost of the edge node.
Optionally, the safe operation constraint of the camera includes: the safety coefficient of the camera is larger than the minimum safety coefficient of the camera, and the safety coefficient of the camera is evaluated and calculated through at least one of regional theft rate, regional security number, safety matching in camera installation matching, regional future municipal planning and regional environment.
Optionally, the yield constraint of the data includes: the yield of valid data is greater than the minimum amount of valid data;
The data source constraints include: the distance between any two cameras of camera type i is greater than the field of view of a single camera of camera type i.
Optionally, the building the multi-stage joint planning model with minimum investment cost and running cost as objective functions includes:
establishing a two-stage joint planning model of four-network coupling of a camera network, an edge node network, a public safety management network and a communication management network, wherein the two-stage joint planning model takes minimized investment cost and operation cost as objective functions, and the two-stage joint planning model meets safety operation constraint of a camera and an edge node, stable operation constraint of data communication, total data source constraint, multipath constraint of the camera and the edge node and nonlinear constraint;
carrying out linearization conversion on nonlinear constraint in the two-stage combined planning model to obtain a mixed integer planning model;
and based on the mixed integer programming model, converting the mixed integer programming model in a mode of adding unpredictable constraint to obtain a multi-stage collaborative programming model.
Optionally, the nonlinear constraint includes: at least one of tidal effects constraints, data source to target occurrence period constraints are communicated.
Optionally, the unpredictable constraint includes: at least one of power supply shutdown constraint and equipment failure constraint.
In a second aspect, an embodiment of the present invention further provides a co-planning apparatus for a camera and an edge node, where the apparatus includes:
the model building module is used for building a multi-stage joint planning model with minimum investment cost and running cost as objective functions under the condition of ensuring that the safety running constraint of the camera and the edge node, the stable running constraint of data communication, the total data source constraint and the multi-path constraint of the camera and the edge node are met;
the scheme determining module is used for determining a collaborative planning scheme of the camera and the edge node according to the multi-stage joint planning model;
the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node;
the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server;
the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints;
The multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node.
In a third aspect, an embodiment of the present invention provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps in the co-planning method of the camera and the edge node provided by the embodiment of the invention when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements steps in a method for collaborative planning of a camera and an edge node provided by an embodiment of the present invention.
In the embodiment of the invention, a multi-stage joint planning model with minimum investment cost and running cost as objective functions is established under the condition of ensuring that the safety running constraint of the camera and the edge node, the stable running constraint of data communication, the total data source constraint and the multi-path constraint of the camera and the edge node are met; determining a collaborative planning scheme of the camera and an edge node according to the multi-stage joint planning model; the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node; the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server; the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints; the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node. Through data type constraint, data yield constraint and data source constraint in the multi-stage joint planning model, reasonable collaborative site selection can be carried out on cameras and edge nodes, and equipment planning guidance is provided for data volume supply and demand balance required by smart city construction under the condition of reducing equipment investment cost and operation cost.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a co-planning method for a camera and an edge node according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for creating a multi-stage joint planning model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for generating a scene by using a node tree method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a decision based on unpredictable constraints provided by an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a collaborative planning apparatus for camera and edge node according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a model building module according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a co-planning method for a camera and an edge node according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
101. under the condition of ensuring that the safe operation constraint of the camera and the edge node, the stable operation constraint of data communication, the total data source constraint and the multipath constraint of the camera and the edge node are met, a multi-stage joint planning model taking the minimum investment cost and the minimum operation cost as objective functions is established.
In the embodiment of the present invention, the security operation constraint of the camera and the edge node specifically includes: the security operation constraints of the camera and the security operation constraints of the edge node. The safe operation of the camera means that the camera is not damaged by other people as much as possible and is not damaged by environmental reasons as much as possible, for example, the camera is installed at a height which is easy to be contacted by any person, is installed at a position which is easy to accumulate water or is often influenced by high wind, and the like.
The stable operation constraint of the data communication includes: communication stability operation constraints of the camera and the edge node, and communication stability operation constraints of the edge node and the server. The stable operation of the communication between the camera and the edge node and the stable operation of the communication between the edge node and the server refer to the avoidance of magnetic field interference. The communication mode between the camera and the edge node can be wire communication or wireless communication, and the communication mode between the edge node and the server can also be wire communication or wireless communication. The server may be a local server or a cloud server. The wireless communication means may include, but is not limited to, 3G/4G/5G connections, wiFi connections, bluetooth connections, wiMAX connections, zigbee connections, UWB (ultra wideband) connections, and other now known or later developed wireless connection means.
The data source total constraint specifically includes: data type constraints, yield constraints of data, and constraints of data origin.
The data type may be a type of vehicle image data, pedestrian image data, object image data, place image data, or the like. The yield of the data may be the amount of image data captured by the camera, the amount of image data actually processed by the edge node, etc. The above-mentioned data sources refer to a camera distribution area, a distribution area of edge nodes, and the like.
The multi-path constraint of the camera and the edge node specifically comprises: distance constraint of camera and edge node, number constraint of camera and edge node. The video shot by the cameras is transmitted to the edge node for calculation in real time, the edge node can calculate and process the video shot by the multi-path cameras in real time through multithreading, the number of threads in the edge node is related to the number of cameras responsible for the edge node, and the more the supporting threads, the higher the requirement of edge node equipment is.
The multiple phases in the multi-phase joint planning model refer to multiple time periods, and can be understood as planning time periods or planning years, such as 5 years, for example, the annual co-planning of cameras and edge nodes.
The objective function with minimum investment cost and running cost can be expressed by the following formula (1):
minF＝IC+OC (1)
in equation (1), IC and OC represent the investment cost and the running cost within a planned year, respectively.
Alternatively, the investment cost may include: camera equipment investment cost for camera type i, camera installation mating investment cost for camera type i, investment equipment cost for edge nodes, and the like. The camera types may include normal cameras, traffic cameras, infrared cameras, depth cameras, custom cameras, etc., with different types of cameras producing different investment costs. For different cameras, the installation matching can be the same or different, for example, the traffic cameras need to be installed on the cross bars of the intersections, so the cross bars are matched with the installation of the traffic cameras, and the cross bars need to be installed at the intersections without the cross bars, so that the investment of camera installation matching is generated. The edge node may be an image processing general type edge node, and it is understood that multiple image processing modes are integrated on one node.
Specifically, the investment cost can be expressed as the following formula (2):
in formula (2), CIC i 、EIC、GIC g The investment cost of the camera installation matching g is respectively the camera equipment with the camera type i, the edge node equipment and the camera with the camera type i; x is x gt The construction state of the camera installation matching g representing the camera type i in the t planning year is 0 or 1 variable, the value of 1 represents construction, and 0 represents non-construction; x is x n Representing the deployment address of the camera, taking 0 or 1 variable, wherein a value of 1 represents the construction, and 0 represents the non-construction; y is n Representing the deployment address of the edge node, taking a 0 or 1 variable, wherein a value of 1 represents the construction, and 0 represents the non-construction; NC represents a set of camera types, NB represents a set of cameras to be planned, NE represents a set of edge nodes to be planned, and NG represents a set of camera installation accessories to be planned.
Alternatively, the operation cost may include: the camera operation maintenance cost of the camera type i, the camera installation matching maintenance cost of the camera type i, the communication cost of the camera and the communication mode of the edge node j, the communication cost of the communication mode of the edge node and the server k, the operation maintenance cost of the edge node and the like.
In the embodiment of the invention, the operation and maintenance costs of different camera types are different, the corresponding camera installation and matching maintenance costs of the corresponding different camera types are also different, the operation cost generated by the communication modes of the camera and the edge node is different according to the different network operators or the different operation platforms, and the operation cost generated by the communication modes of the edge node and the server is also different according to the different network operators or the different operation platforms.
Specifically, the above running cost can be expressed as the following formula (2):
in formula (3), COC t,j 、COP t,i 、EOC t,k 、EOP t 、GIC g 、GOP t,g The method respectively represents the communication cost of the communication mode of the camera and the edge node in the t planning year, the operation and maintenance cost of the camera with the camera type of i in the t planning year, the communication cost of the edge node and the server in the t planning year, the operation and maintenance cost of the edge node in the t planning year and the installation and matching maintenance cost of the camera with the camera type of i in the t planning year, NC represents a type set of the camera, and MC represents a set of the communication modes.
102. And determining a collaborative planning scheme of the camera and the edge node according to the multi-stage joint planning model.
In the embodiment of the invention, the input data is pre-prepared candidate data, and the candidate data can be a camera type set, a camera set to be planned, an edge node set to be planned, a camera installation matching set to be planned, a communication mode set and the like, and the input data also comprises planning years, expected data output, data types and the like.
Input data is input into the multi-stage joint planning model, and the optimal planning scheme in the candidate data can be solved through a model solving tool. The model solution may be a Cplex solver or other mathematical model solver.
The collaborative planning scheme of the camera and the edge node comprises camera site selection, camera type, edge node site selection, camera installation matching, communication mode selection and the like.
In the embodiment of the invention, a multi-stage joint planning model with minimum investment cost and running cost as objective functions is established under the condition of ensuring that the safety running constraint of the camera and the edge node, the stable running constraint of data communication, the total data source constraint and the multi-path constraint of the camera and the edge node are met; determining a collaborative planning scheme of the camera and an edge node according to the multi-stage joint planning model; the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node; the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server; the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints; the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node. Through data type constraint, data yield constraint and data source constraint in the multi-stage joint planning model, reasonable collaborative site selection can be carried out on cameras and edge nodes, and equipment planning guidance is provided for data volume supply and demand balance required by smart city construction under the condition of reducing equipment investment cost and operation cost.
Optionally, referring to fig. 2, fig. 2 is a flowchart of a method for establishing a multi-stage joint planning model according to an embodiment of the present invention, as shown in fig. 2, including the following steps:
201. a four-network coupled two-phase joint planning model M1 of a camera network, an edge node network, a public safety management network and a communication management network is established.
The two-stage collaborative planning model M1 uses minimized investment cost and operation cost as objective functions, and the two-stage collaborative planning model M1 satisfies safe operation constraint of the camera and the edge node, stable operation constraint of data communication, total data source constraint, multi-path constraint of the camera and the edge node and nonlinear constraint.
The investment costs include: camera equipment investment cost for camera type i, camera installation mating investment cost for camera type i, and investment equipment cost for edge nodes.
The operation cost includes: the method comprises the steps of camera operation maintenance cost with the camera type of i, camera installation matching maintenance cost with the camera type of i, communication cost with the communication mode of the camera and the edge node of j, communication cost with the communication mode of the edge node and the server of k and operation maintenance cost of the edge node.
Optionally, the two-stage joint planning model M1 further satisfies an equipment investment constraint, where the equipment investment constraint is defined as that after the camera to be planned, the edge node to be planned and the camera to be planned are put into operation in the t-th planning year, the operation state of the camera to be planned is kept unchanged all the time in the following planning year. The investment constraint of camera equipment is expressed by the constraint expression (4) as follows:
wherein x is ct Representing the construction state of a camera c with a camera type i in the t planning year, wherein a 0-1 variable with a value of 1 represents construction, 0 represents non-construction, NB represents a set of cameras to be planned, and NC represents a set of camera types.
The edge node equipment investment constraint has the constraint expression of the following formula (5):
wherein x is et Representing the construction state of the edge node e in the t planning year, wherein a 0-1 variable has a value of 1 to represent the construction, 0 represents the non-construction, and NE represents the set of the edge nodes to be planned.
The investment constraint of the camera installation matching equipment to be planned is expressed by the constraint expression of the following formula (6):
wherein x is gt The construction state of the camera installation matching g with the camera type i in the t planning year is represented, the 0-1 variable is valued as 1 to represent construction, 0 to represent non-construction, NG to represent a set of camera installation matching to be planned, and NC to represent a set of camera types.
In the two-stage joint planning model M1, the planning scheme of the current planning year can be used as the existing operation data of the next planning year.
Alternatively, in the two-stage joint planning model M1, the safe operation constraint of the camera and the edge node can be expressed by the following equation (7):
wherein S is th Representing a safety constraint threshold, an SC as described above i Representing the security factor of the camera, SE representing the security factor of the edge node, NC representing the set of types of cameras. Wherein the safe operation constraint of the camera comprises: the safety coefficient of the camera is larger than the minimum safety coefficient of the camera, and the safety coefficient of the camera is evaluated and calculated through at least one of regional theft rate, regional security number, safety matching in camera installation matching, regional future municipal planning and regional environment. Specifically, the safety factor of the camera can be expressed by the following expression (8):
wherein SC is provided with i Representing the security factor of the camera, the TSC t The PSC represents a security factor based on regional theft rate in the t planning year t Safety factor representing the number of regional security based on the t planning year, and GSC t i Representing the safety factor of the safety match based on the camera installation match in the t planning year, the FSC t Representing the safety factor of future municipal planning based on the area in the t planning year, the ESC t Representing the safety factor based on the regional environment for the t planning year, NC represents the set of camera types.
The security operation constraint of the edge node includes: the safety coefficient of the edge node is larger than the minimum safety coefficient of the edge node, and the edge node is generally arranged indoors and has larger municipal planning in the future relative to the received area, such as disassembly, mains supply transformation and the like, so the safety coefficient of the edge node can be expressed by the following formula (9):
wherein FSE (FSE) t Representing the safety factor of future municipal planning based on the region in the t planning year.
Optionally, the data source total constraint specifically includes: data type constraints, yield constraints of data, and constraints of data origin. Further, the yield constraints of the data include: the yield of valid data is greater than the minimum amount of valid data. The data source constraints include: the distance between any two cameras of camera type i is greater than the field of view of a single camera of camera type i.
Specifically, the above data type constraint can be expressed by the following expression (10):
wherein D is as described above T Representing data type, as described aboveRepresenting the minimum number of categories of data types, and NT represents the set of data types.
Specifically, the yield constraint of the above data can be expressed by the following formula (11):
wherein D is as described above F Representing valid data, D TFi Effective data of the representative data type T and the camera type i, which are described aboveRepresenting the minimum effective data size. The NT represents a set of data types, and the NC represents a set of camera types.
Specifically, the above data origin constraint can be expressed by the following formula (12):
wherein, the aboveRepresenting the distance between camera R and camera S of camera type i, x as described above Ri And y is Ri Representing the address coordinates of a camera R of camera type i, x being the above Si And y is Si Representing the address coordinates of a camera S of camera type i, as described aboveThe minimum field of view range of a camera of camera type i represents the above NC represents the set of camera types and NB represents the set of cameras to be planned.
Optionally, the multi-path constraint between the camera and the edge node specifically includes: distance constraint of camera and edge node, number constraint of camera and edge node. The distance between the camera and the edge node is used for ensuring that the camera is in the receiving range of the edge node, and the quantity constraint of the camera and the edge node is used for ensuring that each edge node carries out parallel calculation on a preset quantity of cameras.
Specifically, the distance constraint between the camera and the edge node can be expressed by the following equation (13):
wherein D is as described above CE Representing the distance between the camera C and the edge node E, x is as described above C And y is C Representing the address coordinates of camera C, x E And y is E Representing the address coordinates of the edge node E, as described aboveRepresenting the maximum reception range of the edge node E, the NC represents the type set of the camera, and the NB represents the rule to be setThe set of cameras is drawn.
Specifically, the above-mentioned number constraint of cameras and edge nodes can be expressed by the following equation (14):
wherein, C is as described above EC Representing the number of connections of the edge node E to the camera, as described aboveRepresenting the maximum number of connections of the edge node E to the camera, D CE Representing the distance between camera C and edge node E, above +.>Representing the maximum reception range of the edge node E, NC represents the set of camera types, NB represents the set of cameras to be planned.
The nonlinear constraint includes: at least one of tidal effects constraints, data source to target occurrence period constraints are communicated. It should be noted that the above-mentioned constraint of the communication tidal effect is applicable to a non-private network, which means that in a certain period of time, because there are many users using network transmission resources, the computing resources of the communication base station or router are short, which causes the communication network to be blocked or blocked, and the success rate of data transmission is reduced. The constraint that the data source corresponds to the occurrence time of the target refers to that some targets only occur in a specific time period, for example, a vehicle is limited in time and a vehicle can pass through a road, and the vehicle can only appear on the passing road, or the data source caused by going to and from work corresponds to the dense targets.
Specifically, the above communication tidal effect constraint can be expressed by the following equation (15):
wherein the DP is CE Representing the amount of data, DP, of camera C when transmitting data to edge node E C Representing the amount of outgoing data from camera C, DP E Representing the amount of received data of edge node E, K CE As a constant, the NE represents the set of edge nodes to be planned, and NB represents the set of cameras to be planned.
Further, the above sgn (DP C ,DP E ) The expression can be performed by the following formula (16):
wherein DP C Representing the amount of outgoing data from camera C, DP E Representing the amount of received data for edge node E, NE represents the set of edge nodes to be planned and NB represents the set of cameras to be planned.
Specifically, the above-described data source-to-target occurrence period constraint can be expressed by the following equation (17):
wherein the HF is as described above C Representing the number of targets predicted to be highest for capture by camera C, LF described above C Representing the estimated minimum number of snap shots, DP, of camera C C Representing the amount of outgoing data from camera C.
202. And carrying out linearization conversion on nonlinear constraint in the two-stage combined planning model M1 to obtain a mixed integer planning model M2.
In an embodiment of the present invention, the equation (15) may be transformed and relaxed into the following equation (18) by the McCormick envelope method:
Wherein, the above-mentioned theta CE Angle coefficient of second order cone equation, DP described above CE Representing the amount of data when camera C transmits data to edge node E，K CE As a constant, the NE represents the set of edge nodes to be planned, and NB represents the set of cameras to be planned.
Further, the above equation (18) is subjected to conversion relaxation to obtain the following equation (19):
wherein, the above-mentioned theta CE Angle coefficient of second order cone equation, DP described above CE Representing the amount of data, K, when camera C transmits data to edge node E CE As a constant, the NE represents the set of edge nodes to be planned, and NB represents the set of cameras to be planned.
Further, the equation (17) may be transformed and relaxed into the following equation (20):
wherein the HF is as described above C Representing the number of targets predicted to be highest for capture by camera C, LF described above C Representing the estimated minimum number of snap shots, DP, of camera C C Representing the amount of outgoing data from camera C.
It can be seen that the above formula (19) has the same structure as the formula (20), specifically as shown in the following formula (21):
the equation (21) may be linearly converted by an approximation method, and specifically the equation (21) may be linearly converted by a polyhedral approximation method, and the obtained equation (22) is as follows:
wherein L represents additional constraints and variations in the linearization 21 The number of quantities, the error of linearization will decrease as L increases. Epsilon l And eta l Is a linearization variable.
Taking equation 1 as an objective function and equations 2 to 17 as constraint conditions, a general form of a mixed integer linear programming model M2 is obtained, and the general form of M2 is shown as the following equation (23):
wherein t represents the t-th stage of the mixed integer linear programming model M2, and x is the same as the above t ,y t ,δ t ,b t The A, B, E, F is a parameter of the mixed integer linear programming model M2, which is a variable of the mixed integer linear programming model M2.
203. Based on the mixed integer programming model M2, the mixed integer programming model M2 is converted by adding unpredictable constraints, resulting in a multi-stage collaborative programming model M3.
In an embodiment of the present invention, the unpredictable constraints include: at least one of power supply shutdown constraint and equipment failure constraint. The uncertainty of power supply outage and equipment failure can be characterized by different nodes that can be generated by a node tree method and reduced to a number, as shown in fig. 3. In the reduced node tree of fig. 3, each node located at a leaf node has a unique sequence of implementation uncertainties, represented by a branch from the root node to the leaf node. Thus, the multi-stage problem is divided into N sub-problems.
It should be noted that the above-mentioned unpredictable constraint is added to indicate that the investment decisions corresponding to the scenario indistinguishable before the stage t should be the same, as shown in fig. 4. By adding unpredictable constraints, the original mixed integer linear programming model M2 can be rewritten as a multi-stage collaborative programming model M3, the general form of which is represented by the following equation (24):
the model can be modeled and programmed in mathematical optimization software GAMS, and solved by a commercial solver Cplex to finally obtain the annual collaborative planning of the camera and the edge node with minimum investment cost and running cost in the planning year.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a co-planning apparatus for a camera and an edge node according to an embodiment of the present invention, as shown in fig. 5, the apparatus includes:
the model building module 501 is configured to build a multi-stage joint planning model with minimum investment cost and running cost as objective functions under the condition of ensuring that the safe running constraint of the camera and the edge node, the stable running constraint of data communication, the total data source constraint and the multi-path constraint of the camera and the edge node are met;
a solution determining module 502, configured to determine a collaborative planning solution of the camera and an edge node according to the multi-stage joint planning model;
The safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node;
the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server;
the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints;
the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node.
Optionally, the investment cost includes: camera equipment investment cost for camera type i, camera installation mating investment cost for camera type i, and investment equipment cost for edge nodes.
Optionally, the running cost includes: the method comprises the steps of camera operation maintenance cost with the camera type of i, camera installation matching maintenance cost with the camera type of i, communication cost with the communication mode of the camera and the edge node of j, communication cost with the communication mode of the edge node and the server of k and operation maintenance cost of the edge node.
Optionally, the safe operation constraint of the camera includes: the safety coefficient of the camera is larger than the minimum safety coefficient of the camera, and the safety coefficient of the camera is evaluated and calculated through at least one of regional theft rate, regional security number, safety matching in camera installation matching, regional future municipal planning and regional environment.
Optionally, the yield constraint of the data includes: the yield of valid data is greater than the minimum amount of valid data;
the data source constraints include: the distance between any two cameras of camera type i is greater than the field of view of a single camera of camera type i.
Optionally, as shown in fig. 6, the model building module 501 includes:
the model building unit 5011 is configured to build a two-stage joint planning model M1 of four-network coupling of a camera network, an edge node network, a public safety management network, and a communication management network, where the two-stage joint planning model M1 uses minimized investment cost and operation cost as objective functions, and the two-stage joint planning model M1 satisfies a safe operation constraint of a camera and an edge node, a stable operation constraint of data communication, a total data source constraint, a multi-path constraint of the camera and the edge node, and a nonlinear constraint;
the first model conversion unit 5012 is configured to perform linearization conversion on the nonlinear constraint in the two-stage joint planning model M1 to obtain a mixed integer planning model M2;
the second model conversion unit 5013 is configured to obtain a multi-stage collaborative planning model M3 based on the mixed integer planning model M2 by converting the mixed integer planning model M2 by adding unpredictable constraints.
Optionally, the nonlinear constraint includes: at least one of tidal effects constraints, data source to target occurrence period constraints are communicated.
Optionally, the unpredictable constraint includes: at least one of power supply shutdown constraint and equipment failure constraint.
It should be noted that, the co-planning device for the camera and the edge node provided by the embodiment of the invention can be applied to a mobile phone, a monitor, a computer, a server and other devices capable of performing co-planning on the camera and the edge node.
The collaborative planning device for the camera and the edge node provided by the embodiment of the invention can realize each process realized by the collaborative planning method for the camera and the edge node in the embodiment of the method, and can achieve the same beneficial effects. In order to avoid repetition, a description thereof is omitted.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 7, including: a memory 702, a processor 701 and a computer program stored on the memory 702 and executable on the processor 701, wherein:
the processor 701 is configured to call a computer program stored in the memory 702, and perform the following steps:
under the condition of ensuring that the safety operation constraint of the camera and the edge node, the stable operation constraint of data communication, the total data source constraint and the multipath constraint of the camera and the edge node are met, a multi-stage joint planning model taking the minimum investment cost and the minimum operation cost as objective functions is established;
Determining a collaborative planning scheme of the camera and an edge node according to the multi-stage joint planning model;
the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node;
the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server;
the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints;
the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node.
Optionally, the investment cost includes: camera equipment investment cost for camera type i, camera installation mating investment cost for camera type i, and investment equipment cost for edge nodes.
Optionally, the running cost includes: the method comprises the steps of camera operation maintenance cost with the camera type of i, camera installation matching maintenance cost with the camera type of i, communication cost with the communication mode of the camera and the edge node of j, communication cost with the communication mode of the edge node and the server of k and operation maintenance cost of the edge node.
Optionally, the safe operation constraint of the camera includes: the safety coefficient of the camera is larger than the minimum safety coefficient of the camera, and the safety coefficient of the camera is evaluated and calculated through at least one of regional theft rate, regional security number, safety matching in camera installation matching, regional future municipal planning and regional environment.
Optionally, the yield constraint of the data includes: the yield of valid data is greater than the minimum amount of valid data;
the data source constraints include: the distance between any two cameras of camera type i is greater than the field of view of a single camera of camera type i.
Optionally, the building of the multi-stage joint planning model with minimum investment cost and running cost as objective functions performed by the processor 701 includes:
establishing a two-stage joint planning model M1 of four-network coupling of a camera network, an edge node network, a public safety management network and a communication management network, wherein the two-stage joint planning model M1 takes minimized investment cost and operation cost as objective functions, and the two-stage joint planning model M1 meets safe operation constraint of a camera and an edge node, stable operation constraint of data communication, total data source constraint, multi-path constraint of the camera and the edge node and nonlinear constraint;
Carrying out linearization conversion on nonlinear constraint in the two-stage combined planning model M1 to obtain a mixed integer planning model M2;
based on the mixed integer programming model M2, the mixed integer programming model M2 is transformed by adding unpredictable constraints to obtain a multi-stage collaborative programming model M3.
Optionally, the nonlinear constraint includes: at least one of tidal effects constraints, data source to target occurrence period constraints are communicated.
Optionally, the unpredictable constraint includes: at least one of power supply shutdown constraint and equipment failure constraint.
It should be noted that the electronic device may be a mobile phone, a monitor, a computer, a server, etc. that may be used to perform collaborative planning of a camera and an edge node.
The electronic device provided by the embodiment of the invention can realize each process realized by the collaborative planning method of the camera and the edge node in the embodiment of the method, can achieve the same beneficial effects, and is not repeated here for avoiding repetition.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements each process of the collaborative planning method for a camera and an edge node provided by the embodiment of the invention, and can achieve the same technical effect, so that repetition is avoided, and no further description is provided here.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM) or the like.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.

Claims (10)

1. The collaborative planning method for the camera and the edge node is characterized by comprising the following steps of:
under the condition of ensuring that the safety operation constraint of the camera and the edge node, the stable operation constraint of data communication, the total data source constraint and the multipath constraint of the camera and the edge node are met, a multi-stage joint planning model taking the minimum investment cost and the minimum operation cost as objective functions is established;
Determining a collaborative planning scheme of the camera and an edge node according to the multi-stage joint planning model;
the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node;
the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server;
the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints;
the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of the camera and the edge node, and quantity constraint of the camera and the edge node;
the establishing a multi-stage joint planning model with minimum investment cost and running cost as objective functions comprises the following steps:
establishing a two-stage joint planning model of four-network coupling of a camera network, an edge node network, a public safety management network and a communication management network, wherein the two-stage joint planning model takes minimized investment cost and operation cost as objective functions, and the two-stage joint planning model meets safety operation constraint of a camera and an edge node, stable operation constraint of data communication, total data source constraint, multipath constraint of the camera and the edge node and nonlinear constraint;
Carrying out linearization conversion on nonlinear constraint in the two-stage combined planning model to obtain a mixed integer planning model;
and based on the mixed integer programming model, converting the mixed integer programming model by adding unpredictable constraint to obtain a multi-stage joint programming model.
2. The method of claim 1, wherein the investment cost comprises:
camera equipment investment cost for camera type i, camera installation mating investment cost for camera type i, and investment equipment cost for edge nodes.
3. The method of claim 2, wherein the operating cost comprises:
the method comprises the steps of camera operation maintenance cost with the camera type of i, camera installation matching maintenance cost with the camera type of i, communication cost with the communication mode of the camera and the edge node of j, communication cost with the communication mode of the edge node and the server of k and operation maintenance cost of the edge node.
4. The method of claim 3, wherein the safe operating constraints of the camera include:
the safety coefficient of the camera is larger than the minimum safety coefficient of the camera, and the safety coefficient of the camera is evaluated and calculated through at least one of regional theft rate, regional security number, safety matching in camera installation matching, regional future municipal planning and regional environment.
5. The method of claim 4, wherein the yield constraint of the data comprises:
the yield of valid data is greater than the minimum amount of valid data;
the data source constraints include: the distance between any two cameras of camera type i is greater than or equal to the field of view of a single camera of camera type i.
6. The method of claim 1, wherein the nonlinear constraint comprises: at least one of tidal effects constraints, data source to target occurrence period constraints are communicated.
7. The method of claim 1, wherein the unpredictable constraint comprises: at least one of power supply shutdown constraint and equipment failure constraint.
8. A co-planning apparatus for a camera and an edge node, the apparatus comprising: the model building module is used for building a multi-stage joint planning model with minimum investment cost and running cost as objective functions under the condition of ensuring that the safety running constraint of the camera and the edge node, the stable running constraint of data communication, the total data source constraint and the multi-path constraint of the camera and the edge node are met; the establishing a multi-stage joint planning model with minimum investment cost and running cost as objective functions comprises the following steps: establishing a two-stage joint planning model of four-network coupling of a camera network, an edge node network, a public safety management network and a communication management network, wherein the two-stage joint planning model takes minimized investment cost and operation cost as objective functions, and the two-stage joint planning model meets safety operation constraint of a camera and an edge node, stable operation constraint of data communication, total data source constraint, multipath constraint of the camera and the edge node and nonlinear constraint; carrying out linearization conversion on nonlinear constraint in the two-stage combined planning model to obtain a mixed integer planning model; based on the mixed integer programming model, converting the mixed integer programming model in a mode of adding unpredictable constraint to obtain a multi-stage joint programming model;
The scheme determining module is used for determining a collaborative planning scheme of the camera and the edge node according to the multi-stage joint planning model;
the safe operation constraint of the camera and the edge node specifically comprises: safety operation constraint of camera and safety operation constraint of edge node;
the steady operation constraints of the data communication include: communication stable operation constraint of the camera and the edge node, and communication stable operation constraint of the edge node and the server;
the data source total constraint specifically comprises: data type constraints, yield constraints of data, and data origin constraints;
the multi-path constraint of the camera and the edge node specifically comprises the following steps: distance constraint of camera and edge node, number constraint of camera and edge node.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the co-planning method of a camera and an edge node as claimed in any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps in the co-planning method of a camera and an edge node according to any of claims 1 to 7.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104469322A (en) * 2014-12-24 2015-03-25 重庆大学 Camera layout optimization method for large-scale scene monitoring
US9036044B1 (en) * 2013-07-22 2015-05-19 Google Inc. Adjusting camera parameters associated with a plurality of images
CN107066747A (en) * 2017-04-24 2017-08-18 哈尔滨理工大学 A kind of vision measurement network organizing planing method
CN109728605A (en) * 2019-03-01 2019-05-07 西安交通大学 Planing method is combined in wind power plant-power transmission network based on chance constraint IGDT-energy storage
KR20190058230A (en) * 2017-11-20 2019-05-29 (주)포스트미디어 System and method for modeling surveillance camera layout
CN111179438A (en) * 2020-01-02 2020-05-19 广州虎牙科技有限公司 AR model dynamic fixing method and device, electronic equipment and storage medium
CN111416349A (en) * 2020-04-22 2020-07-14 南方电网科学研究院有限责任公司 Collaborative planning method for electrical interconnection comprehensive energy system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8082549B2 (en) * 2006-11-08 2011-12-20 Board Of Regents, The University Of Texas System System, method and apparatus for allocating resources by constraint selection

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9036044B1 (en) * 2013-07-22 2015-05-19 Google Inc. Adjusting camera parameters associated with a plurality of images
CN104469322A (en) * 2014-12-24 2015-03-25 重庆大学 Camera layout optimization method for large-scale scene monitoring
CN107066747A (en) * 2017-04-24 2017-08-18 哈尔滨理工大学 A kind of vision measurement network organizing planing method
KR20190058230A (en) * 2017-11-20 2019-05-29 (주)포스트미디어 System and method for modeling surveillance camera layout
CN109728605A (en) * 2019-03-01 2019-05-07 西安交通大学 Planing method is combined in wind power plant-power transmission network based on chance constraint IGDT-energy storage
CN111179438A (en) * 2020-01-02 2020-05-19 广州虎牙科技有限公司 AR model dynamic fixing method and device, electronic equipment and storage medium
CN111416349A (en) * 2020-04-22 2020-07-14 南方电网科学研究院有限责任公司 Collaborative planning method for electrical interconnection comprehensive energy system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于GIS的公安监控点位规划系统研究与应用;程长河;陈绪;关昆;;中国公共安全(学术版)(第03期);120-124页 *

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