Method and device for generating deployment location map based on Internet of Things lighting system

Abstract

The present invention discloses a method and device for generating a deployment location map based on an Internet of Things lamp system. The method first collects the initial data of each local route lamp group that is triggered; then uses the initial data of each local route lamp group to calculate the lamp coordinates, obtains the initial coordinates of each lamp in each local route lamp group, and generates an initial coordinate map corresponding to each local route lamp group; then optimizes the position of the initial coordinates of each lamp in each initial coordinate map and generates an optimized coordinate map; then converts the relative position relationship between the lamps according to the wireless communication information between the lamps, and uses the relative position relationship to transform the lamp coordinates of each optimized coordinate map, obtains the actual coordinates of all lamps, and generates a lamp deployment location map. The present invention uses the sensing and communication capabilities of lamps to automatically generate a lamp deployment location map of the distribution of lamps in the actual space, without the need for manual measurement, thereby reducing deployment costs.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things and intelligent lighting technology, and in particular to a method and device for generating a deployment location map based on an Internet of Things lighting system. Background Art

[0002] With the development of IoT technology, smart lighting systems are becoming more and more popular in commercial and residential spaces. Smart lighting systems usually include multiple IoT lighting devices with wireless communication capabilities. In the field of smart lighting, IoT lamps as IoT lighting devices will integrate radar, pyroelectric and other sensors to adjust the best lighting plan when people and vehicles are passing by, and realize energy-saving lighting plans when there are no people or vehicles. In order to promote and apply technology, IoT lamps can achieve functions such as free debugging by engineers and automatic operation, but it is not possible to well integrate IoT lamps that do not require debugging and are self-operating into the smart building management platform.

[0003] One of the main technical difficulties at present is the lack of an effective method to generate the installation and deployment location distribution map of the IoT lighting system at low cost and high automation, which limits the further optimization and application of intelligent lighting systems and smart building management. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for generating a deployment location map based on an Internet of Things lamp system, aiming to solve the problem of how to use the built-in sensors and communication capabilities of Internet of Things lamps in the prior art to automatically generate a lamp deployment location map of the distribution of lamps in a space.

[0005] In a first aspect, an embodiment of the present invention provides a method for generating a deployment location map based on an Internet of Things lighting system, comprising:

[0006] Determine the current time period when the current lamp is triggered, obtain other adjacent lamps triggered within the current time period and form a current local route lamp group, obtain the time point data when the current local route lamp group is triggered and generate initial data corresponding to the current local route lamp group;

[0007] The initial data corresponding to each local route lamp group is used to calculate the lamp coordinates through graph theory and heuristic algorithms to obtain the initial coordinates of each lamp in each local route lamp group, and generate an initial coordinate graph corresponding to each local route lamp group;

[0008] The hill climbing algorithm is used to optimize the position of the initial coordinates of each lamp in each initial coordinate map, and an optimized coordinate map corresponding to each local route lamp group is generated;

[0009] The relative position relationship of each lamp in the actual spatial distribution is calculated based on the wireless communication information sent and received between the lamps in each local route lamp group when they are triggered, and the lamp coordinates in the corresponding optimized coordinate diagram are transformed using the relative position relationship to obtain the optimized coordinate diagram after the coordinate transformation, and the optimized coordinate diagrams after the coordinate transformation corresponding to all local route lamp groups are merged to generate a lamp deployment location map;

[0010] The lamp deployment location map is bound to the building map where the Internet of Things lamp system is located.

[0011] In a second aspect, an embodiment of the present invention provides a device for generating a deployment location map based on an Internet of Things lighting system, comprising:

[0012] A data acquisition unit, used to determine the current time period when the current lamp is triggered, acquire other adjacent lamps triggered within the current time period and form a current local route lamp group, acquire the time point data when the current local route lamp group is triggered and generate initial data corresponding to the current local route lamp group;

[0013] An initial coordinate processing unit, used to calculate and process the coordinates of the lamps using the initial data corresponding to each local route lamp group through graph theory and heuristic algorithms, obtain the initial coordinates of each lamp in each local route lamp group, and generate an initial coordinate graph corresponding to each local route lamp group;

[0014] An optimized coordinate processing unit, used for performing position optimization processing on the initial coordinates of each lamp in each initial coordinate map by using a hill climbing algorithm, and generating an optimized coordinate map corresponding to each local route lamp group;

[0015] A map generation unit is used to convert the relative position relationship of each lamp in the actual spatial distribution according to the wireless communication information sent and received between the lamps in each local route lamp group when they are triggered, and transform the lamp coordinates in the corresponding optimized coordinate map using the relative position relationship to obtain the optimized coordinate map after the coordinate transformation, and merge the optimized coordinate maps after the coordinate transformation corresponding to all local route lamp groups to generate a lamp deployment location map;

[0016] A map binding unit is used to bind the lamp deployment location map with the building map where the Internet of Things lamp system is located.

[0017] The beneficial effects of the embodiments of the present invention are: utilizing the sensing and communication capabilities of lamps to automatically generate a lamp deployment location map of the distribution of lamps in a space, without the need for manual measurement, thereby reducing deployment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of a sub-process of a method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of another sub-process of the method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of another sub-process of the method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of another sub-process of the method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0024] Figure 6 A schematic diagram of another sub-process of the method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0025] Figure 7 A schematic diagram of another sub-process of the method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0026] Figure 8 A schematic diagram of another sub-process of the method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0027] Fig. 9 A schematic block diagram of a device for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0028] Fig.10 A schematic diagram of an embodiment scenario outline provided for an embodiment of the present invention;

[0029] Fig.11 The embodiment of the present invention provides Fig.10 The topological map of the deployment locations corresponding to the scenario. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0032] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0033] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0034] See also Figure 1 , Figure 1 A schematic diagram of a flow chart of a method for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention;

[0035] like Figure 1 As shown, the method includes steps S101 to S105.

[0036] S101, determining the current time period when the current lamp is triggered, obtaining other adjacent lamps triggered within the current time period and forming a current local route lamp group, obtaining the time point data when the current local route lamp group is triggered and generating initial data corresponding to the current local route lamp group;

[0037] In this step, the IoT lighting system is formed by a number of IoT lightings (hereinafter referred to as lightings) communicating and interacting with each other. Each lighting can have a built-in wireless communication module, a sensor (such as a radar motion sensor) and a processor with storage and processing computing capabilities. The wireless communication module and processor can use an integrated LT5910 chip. The LT5910 chip has 2.4G wireless communication capabilities and the ability to run program algorithms and store data. Each lighting stores a unique device address (i.e., a unique ID);

[0038] In this step, the lamp is triggered when it detects the detected object (such as a person or vehicle) through the sensor. During the time period when each lamp is triggered, if the detected object continues to trigger other adjacent lamps during the time period, a list of adjacent lamps corresponding to each lamp can be formed; and the time point data of each lamp and its adjacent lamps can be formed into the initial data corresponding to each lamp. By obtaining the initial data of each lamp, data support can be provided for the subsequent calculation of the coordinate position of each lamp.

[0039] S102, using graph theory and heuristic algorithms to calculate the lamp coordinates using the initial data corresponding to each local route lamp group, to obtain the initial coordinates of each lamp in each local route lamp group, and to generate an initial coordinate graph corresponding to each local route lamp group.

[0040] S103, using a hill climbing algorithm to optimize the position of the initial coordinates of each lamp in each initial coordinate map, and generating an optimized coordinate map corresponding to each local route lamp group.

[0041] S104. Calculate the relative position relationship of each lamp in the actual spatial distribution based on the wireless communication information sent and received between the lamps in each local route lamp group when they are triggered, and use the relative position relationship to transform the lamp coordinates in the corresponding optimized coordinate diagram to obtain the optimized coordinate diagram after the coordinate transformation, and merge the optimized coordinate diagrams after the coordinate transformation corresponding to all local route lamp groups to generate a lamp deployment location map.

[0042] In steps S102 to S104, the initial data is used to sequentially calculate, optimize and transform the coordinates of the lamps, and a lamp deployment location map of the distribution of each lamp in the actual space (ie, the real space in the real world) can be obtained.

[0043] S105: Bind the lamp deployment location map with the building map where the IoT lamp system is located.

[0044] In this embodiment, the initial data of each local route lighting group is first obtained; then the initial data of each local route lighting group is used to generate an initial coordinate map corresponding to each local route lighting group through graph theory and heuristic algorithms; and the lighting positions of each initial coordinate map are iteratively optimized multiple times through a hill climbing algorithm to generate an optimized coordinate map corresponding to each local route lighting group, and then each optimized coordinate map is adjusted to the overall coordinate system to form a lighting deployment location map of each lighting fixture in the actual space distribution, and the lighting deployment location map is bound to the building map. This embodiment reduces the work of manual measurement and has the advantages of high automation efficiency and low deployment cost. It can also automatically adapt to the addition, removal or repositioning of lighting fixtures, and can continuously improve map accuracy under long-term data accumulation. Provide basic data for advanced lighting control and spatial analysis.

[0045] In some embodiments, for the initial data of all lamps obtained in step S101, if the initial data is particularly large, the local IoT device and general server-type devices may not be able to process it. The initial data can be uploaded to the cloud server, and the cloud server performs subsequent processing of the initial data and outputs the results. After the cloud server outputs the results, this part of the storage space and computing power can be completely released. If the local server runs this method to output the results, the storage space and calculation examples may be idle. Furthermore, the initial data of each lamp can be uploaded to the cloud server regularly. For example, the initial data from 0:00 to 6:00 in the morning is a group, and the initial data is uploaded to the cloud server every 3 hours at other times.

[0046] In one embodiment, if Figure 2 As shown, step S101 includes:

[0047] S201, when the current lamp is triggered by the detected object, the current timestamp when it is triggered is recorded and a triggered wireless communication signal is sent to surrounding lamps;

[0048] S202, selecting a previous preset time period and a next preset time period of the current timestamp, and merging them into the current time period;

[0049] S203, obtaining other lamps triggered in the surrounding area within the current time period and using them as neighboring lamps of the current lamp to form a lamp group for the current local route;

[0050] S204, obtaining the time point data when each lamp in the current local route lamp group is triggered and generating initial data corresponding to the current local route lamp group, wherein the time point data includes: the timestamp when each lamp is triggered, the device address of each lamp, and the wireless communication information sent and received between each lamp.

[0051] In this embodiment, when the detected object passes through the detection range of the current lamp, the current lamp detects the moving detected object through its own sensor, thereby triggering the current lamp and recording the current timestamp. At the same time, the current lamp sends a triggered wireless communication signal to the surrounding lamps through its own wireless communication module, and takes the previous preset time period t1 and the next preset time period t2 of the current timestamp, and adds them to form the current time period T. Within the current time period T, if there are other lamps around the current lamp that are also triggered by the detected object, the current lamp will receive the triggered wireless communication signals sent by other lamps triggered around it within the current time period T, and regard the other lamps triggered around it as neighboring lamps of the current lamp, and the current lamp and its neighboring lamps constitute the current local route lamp group corresponding to the current lamp. It should be understood that each lamp corresponds to a local route lamp group. For example, assuming that there are no other lamps between lamps 1, 2, 3, 4 and 5, and lamps 1 to 5 are triggered at 0 seconds, 6 seconds, 13 seconds, 20 seconds and 25 seconds respectively, and considering the sensor detection range of the lamp and the moving speed of the detected object, assuming t1=t2=10 seconds, and lamp 3 is the current lamp, lamp 3 can send triggered wireless communication signals to the surrounding lamps 1, 2, 4 and 5 after being triggered, but lamp 3 only receives wireless communication signals sent by neighboring lamps that are triggered within the current time period T (3 seconds to 23 seconds), that is, lamp 3 can receive wireless communication signals sent by lamps 2 and 4. That is, the local route lamp group corresponding to lamp 3 includes lamps 2 to 4.

[0052] In this embodiment, the time point data when each lamp in the current local route lamp group is triggered is used for subsequent calculation of the triggering order and relative position relationship of each lamp; the time point data refers to the sum of the data of the wireless communication signals sent and received when each lamp is triggered, and may include the ID of each lamp, the time taken for each lamp to send and receive the wireless communication signals, and the communication speed.

[0053] In one embodiment, if Figure 3 As shown, step S102 includes:

[0054] For each local route lighting fixture group, execute the following coordinate calculation process:

[0055] S301, taking the lamp with the most adjacent lamps in the local route lamp group as the origin lamp;

[0056] S302, setting the origin lamp as the origin and establishing a coordinate axis;

[0057] S303, selecting two lamps that are farthest apart from all neighboring lamps of the origin lamp as the farthest lamps, and placing the two farthest lamps at both ends of the X axis of the coordinate axis;

[0058] S304, confirming the neighboring relationship between the remaining other neighboring lamps and the two farthest lamps, placing other neighboring lamps having a neighboring relationship with the corresponding farthest lamps at integer coordinate positions adjacent to the corresponding farthest lamps in the coordinate axis, and generating an initial coordinate diagram of the local route lamp group;

[0059] The coordinate calculation process of steps S301 to S304 is repeated to complete the coordinate calculation of each local route lamp group, and an initial coordinate map corresponding to each local route lamp group is generated.

[0060] In this embodiment, for each local route lamp group, two main data structures can be created, namely, a lamp neighbor relationship dictionary and a lamp coordinate dictionary; the key in the lamp neighbor relationship dictionary is the lamp ID, and the value is the neighboring lamp information of the lamp, and the key in the lamp coordinate dictionary is the lamp ID, and the value is the coordinate of the lamp. Assuming that the local route lamp group corresponding to lamp 3 in the above steps S201~S204 is taken as an example, the lamp neighbor relationship dictionary will store the ID of lamp 3 and the information of neighboring lamps 2 and 4, and the lamp coordinate dictionary will store the coordinate information of lamps 2~4.

[0061] Specifically, an example of the process of creating a lamp neighbor relationship dictionary for each local route lamp group is: using the current lamp ID as the key, and the key value is all the neighboring lamp information of the current lamp ID; it should be noted that in the lamp neighbor relationship dictionary corresponding to each local route lamp group, the lamp neighbor relationship dictionary needs to be filled to ensure the consistency of the neighbor relationship, that is, assuming that lamp 1 regards lamp 2 as a neighbor, then lamp 2 should also regard lamp 1 as a neighbor.

[0062] Specifically, an example of the process of creating a lamp coordinate dictionary for each local route lamp group is as follows:

[0063] First, the origin lamp R is selected using the formula R = argmax(|N(i)|), where N(i) represents the set of neighboring lamps of lamp i, and |N(i)| represents the size of the set of neighboring lamps;

[0064] Then, set the coordinates of the origin lamp R to (0, 0) and establish the coordinate axis, and add the coordinates of the origin lamp R to the lamp coordinate dictionary;

[0065] Then, two lamps (such as lamp A and lamp B) that are farthest apart from each other are selected from the neighboring lamps of the origin lamp R as the farthest lamps and placed at the two ends of the X-axis of the coordinate axis; the farthest lamps can be selected in the following manner: in the triggered time period corresponding to the local route lamp group, the two lamps that take the longest time to wirelessly communicate with the origin lamp in the previous preset period and the next preset period are selected as the two farthest lamps. Assuming that lamp A is selected in the previous preset period, lamp A is set as the farthest lamp and its coordinates are set to (-1, 0), and lamp B is selected in the next preset period, lamp B is set as another farthest lamp and its coordinates are set to (1, 0), and the coordinates of the two farthest lamps are added to the lamp coordinate dictionary;

[0066] Finally, continue to traverse other neighboring lamps of the origin lamp R, and determine their coordinate positions according to the neighboring relationship between other neighboring lamps and the two farthest lamps; for example, if neighboring lamp C is a neighbor of both farthest lamps, then set the coordinate of neighboring lamp C to (0, 1) or (0, -1); for example, if neighboring lamp C is only a neighbor of the farthest lamp A, then set the coordinate of neighboring lamp C to (-1, 1) or (-1, -1); for example, if neighboring lamp C is only a neighbor of the farthest lamp B, then set the coordinate of neighboring lamp C to (1, 1) or (1, -1), and add the coordinate of neighboring lamp C to the lamp coordinate dictionary. Based on this, the coordinates of all neighboring lamps of the origin lamp R are confirmed, and the lamp coordinate dictionary of each local route lamp group can be created.

[0067] In one embodiment, if Figure 4 As shown, step S102 also includes:

[0068] S401, when there is an unplaced lamp that has no neighbor relationship with the two farthest lamps among the remaining neighboring lamps, obtain all placed lamps that have a neighbor relationship with the unplaced lamp;

[0069] S402: when there are multiple placed lamps, calculate the average coordinates of all the placed lamps, and place the unplaced lamps at the integer coordinate position closest to the average coordinate in the coordinate axis;

[0070] S403: When there is only one placed lamp, place the unplaced lamps at adjacent integer coordinate positions around the placed lamps in the coordinate axis.

[0071] In this embodiment, in the aforementioned step S304, there is a situation where the coordinates of the neighboring lamps cannot be confirmed, that is, some neighboring lamps have no neighbor relationship with the two farthest lamps. In this case, the neighboring lamps can be defined as unplaced lamps. The coordinate confirmation method of the unplaced lamps is as follows:

[0072] First, for each unplaced lamp (such as lamp D), find all placed lamps that have a neighboring relationship with the unplaced lamp D;

[0073] Then, if there are multiple placed luminaires that have a neighboring relationship with the unplaced luminaire D, the coordinate averages (x_avg, y_avg) of these placed luminaires are calculated, where x_avg = Σx_i / n, y_avg = Σy_i / n; where (x_i, y_i) represents the coordinates of the placed luminaire i, and n is the number of placed luminaires; the unplaced luminaire D is placed at the integer coordinate closest to the coordinate average, and the coordinate is added to the luminaire coordinate dictionary after placement. It should be noted that the integer coordinate belongs to the unoccupied coordinate;

[0074] Finally, if the number of placed luminaires that has an adjacent relationship with the unplaced luminaire D is only one, then an unoccupied position is selected from multiple (can be eight) adjacent integer coordinates around this placed luminaire, and the unplaced luminaire D is placed on the adjacent integer coordinate, and the coordinate is added to the luminaire coordinate dictionary after placement.

[0075] Based on the coordinate calculation process of the aforementioned steps S301 - S304 and S401 - S403 , the coordinate calculation of each lamp in each local route lamp group is completed, and an initial coordinate map corresponding to each local route lamp group can be generated.

[0076] In one embodiment, if Figure 5 As shown, step S103 includes:

[0077] For each placed luminaire in each initial coordinate map, perform the following iterative optimization process:

[0078] S501, calculating the average coordinates of all neighboring lamps of the placed lamps;

[0079] S502, calculating the Manhattan distance between the coordinate values ​​of the placed lamps and the average coordinate value to obtain a first Manhattan distance;

[0080] S503, calculating the Manhattan distances between a plurality of adjacent integer coordinate values ​​around the placed lamps and the average coordinate value, to obtain a plurality of second Manhattan distances;

[0081] S504, when at least one of the plurality of second Manhattan distances is smaller than the first Manhattan distance and the corresponding adjacent integer coordinates are not occupied, moving the placed lamps to the corresponding adjacent integer coordinates that are not occupied;

[0082] S505, respectively calculating the overall layout scores of the placed lamps before and after they are moved to unoccupied adjacent integer coordinates, and if the overall layout score after the movement is greater than the overall layout score before the movement, canceling the movement operation of the placed lamps;

[0083] The iterative optimization process of steps S501 to S505 is repeatedly executed until the optimization is completed, and an optimization coordinate diagram corresponding to each local route lamp group is generated.

[0084] Specifically, the coordinate optimization process of the placed lamp F is introduced as an example:

[0085] First, get the coordinate averages (x_avg, y_avg) of all neighboring luminaires of the placed luminaire F;

[0086] Then, the Manhattan distance between the placed luminaire F and the coordinate average value (x_avg, y_avg) is calculated to obtain the first Manhattan distance D_current = |x_F - x_avg| + |y_F - y_avg|;

[0087] Then, multiple (e.g., four) adjacent integer coordinate values ​​around the placed lamp F are obtained, and the Manhattan distances between the multiple adjacent integer coordinate values ​​and the coordinate average values ​​(x_avg, y_avg) are respectively calculated to obtain multiple (i.e., four) second Manhattan distances;

[0088] Then, when at least one of the plurality of second Manhattan distances is smaller than the first Manhattan distance and the corresponding adjacent integer coordinates are not occupied, the placed lamp F is moved to the corresponding adjacent integer coordinates that are not occupied;

[0089] Finally, the overall layout score S of the placed luminaire F before and after it is moved to an unoccupied adjacent integer coordinate is calculated, S = Σ(|x_i - x_j| + |y_i - y_j|), where (x_i, y_i) and (x_j, y_j) represent the coordinates of all adjacent luminaire pairs. If the overall layout score S after the move is greater than the overall layout score S before the move, the move operation of the placed luminaire F is canceled.

[0090] Based on this, the coordinate optimization process is iterated multiple times for the above-mentioned placed lamps F. The number of iterations can be adjusted according to the actual situation of the project. The larger the number of lamps in the IoT lamp system or the greater the density of lamp installation and deployment per unit building area, the more iterations are required.

[0091] Furthermore, in the process of iteratively optimizing each placed lamp, there are the following two possible situations.

[0092] The first case is that the placed luminaire is an isolated luminaire without adjacent luminaires (generally refers to an isolated luminaire that is triggered only by itself and no other luminaires are triggered during the triggered time period due to an environment such as a biased position or being blocked). In this case, the optimization cannot be performed according to the process of steps S501 to S505. The placed luminaire can be placed at the integer coordinate farthest from all placed luminaires in the coordinate axis. The distance is defined as D_isolated, D_isolated = min(|x -x_i| + |y - y_i|), where (x, y) is the candidate position of the farthest integer coordinate, and (x_i, y_i) is the coordinate of the placed luminaire i.

[0093] The second situation is that the placement coordinates of multiple placed lamps are the same, that is, the placement positions of multiple placed lamps conflict. Multiple placed lamps with conflicting placement coordinates can be constructed into a conflicting lamp set, and the following is executed: Figure 6 The process of redefining coordinates is shown below:

[0094] S601, comparing the ID size of each conflicting lamp in the conflicting lamp set, retaining the conflicting lamp with the smallest ID in the conflicting coordinates, and updating the conflicting lamp set;

[0095] S602, obtaining a neighboring lamp set of each conflicting lamp in the updated conflicting lamp set;

[0096] S603, calculating the distance relationship between each conflicting lamp and each adjacent lamp in its adjacent lamp set;

[0097] S604, calculating the deviation direction of each conflicting lamp in its set of neighboring lamps;

[0098] S605, searching for a new position based on the distance relationship and the deviation direction, starting from the current conflicting coordinates and in step length, to find at least one unoccupied candidate coordinate position;

[0099] S606, calculating the change in the local conflict degree of each conflicting lamp in the conflicting lamp set after it moves from the current conflicting coordinate to each candidate coordinate position, and moving the conflicting lamp to the candidate coordinate position where the local conflict degree can be reduced;

[0100] S607, when multiple conflicting lamps select the same candidate coordinate position, retain the conflicting lamp with the smallest ID among the multiple conflicting lamps in the candidate coordinate position, and continue to search for a new position according to the step size with the candidate coordinate position as the current starting point until the coordinates of all conflicting lamps are determined.

[0101] In this embodiment, only one conflicting lamp in the conflicting lamp set in step S601 can be retained in the current conflicting coordinates. After the conflicting lamp with the smallest ID is retained in the conflicting coordinates, the remaining conflicting lamps need to re-determine the coordinate positions; assuming that the updated conflicting lamp set is conflicting lamp set B, conflicting lamp set B contains all conflicting lamps that need to be re-positioned. For each conflicting lamp b_i in conflicting lamp set B, continue to perform the following coordinate re-determination operations according to the process of steps S602 to S607:

[0102] First, for each conflicting luminaire b_i, identify its neighboring luminaire set N(b_i); the neighboring luminaire set N(b_i) includes:

[0103] a) The luminaires with determined positions directly adjacent to b_i;

[0104] b) Other conflicting lamps directly adjacent to b_i;

[0105] c) Luminaires adjacent to luminaires in a) and b) take into account the three-level relationship.

[0106] Then, for each conflicting luminaire b_i, calculate its distance relationship with each neighboring luminaire k in the neighboring luminaire set N(b_i), and the distance relationship includes the current distance D(b_i, k) and the expected distance E(b_i, k):

[0107] Current distance D(b_i, k) = |x_b_i - x_k| + |y_b_i - y_k|;

[0108] Expected distance E(b_i, k), the value of expected distance E is determined according to the relationship level between each conflicting luminaire b_i and each adjacent luminaire k: the first-level relationship is 1, the second-level relationship is 2, and the third-level relationship is 3. For example, luminaires a, b, c, and d are adjacent luminaires on the same line. Luminaires a and b are directly adjacent and defined as a first-level relationship (i.e., the value is 1); luminaires a and c are separated by b, but they may also communicate and interact, so they are second-level adjacent and defined as a second-level relationship (i.e., the value is 2); and so on, luminaires a and d are third-level adjacent and defined as a third-level relationship (i.e., the value is 3). It should be noted that the setting of the expected distance E has an important impact on the final result, and it is also possible to consider dynamically adjusting this value according to the type of luminaire or the installation environment.

[0109] Then, calculate the deviation direction vector V_i of each conflicting luminaire b_i:

[0110] a) For each luminaire k with a determined position in the neighboring luminaire set N(b_i), calculate the unit direction vector u_k; u_k = (x_k - x_b_i, y_k - y_b_i) / ||x_k - x_b_i, y_k - y_b_i||;

[0111] b) For each luminaire k with a determined position in the neighboring luminaire set N(b_i), calculate the weighted average vector V_i; V_i = Σ (w_k * u_k) / Σ w_k;

[0112] The conflict weight w_k = 1 / D(b_i, k)² is used to emphasize the impact of close-range lamps. The definition of the conflict weight can be adjusted according to actual conditions. For example, factors such as the brightness and coverage of the lamps can be considered.

[0113] Then, taking the deviation direction vector V_i of each conflicting luminaire b_i as the direction and the current conflicting coordinate position of each conflicting luminaire b_i as the starting point, search for new unoccupied candidate coordinate positions in steps (such as 1 unit) to find at least one unoccupied candidate coordinate position; and calculate the local conflict degree C(x_new, y_new) of each conflicting luminaire b_i after it moves to the candidate coordinate position, C(x_new, y_new) = Σ |D(b_i, k) - E(b_i, k)|, where k ∈ N(b_i); and compare the change in the local conflict degree before and after the movement of the conflicting luminaire b_i; and move the conflicting luminaire b_i to the candidate coordinate position where the local conflict degree can be reduced. It should be noted that if the local conflict degrees of multiple conflicting luminaires b_i can be reduced after they are moved to multiple candidate coordinate positions, the coordinate redetermination operation is completed. If there are multiple conflicting luminaires b_i that have selected the same candidate coordinate position, the following steps are performed:

[0114] a) Keep the lamp with the smallest ID among multiple conflicting lamps b_i at the candidate coordinate position;

[0115] b) Other conflicting lamps b_i continue to search for new candidate coordinate positions;

[0116] c) If a new candidate coordinate position without conflict cannot be found for the remaining conflicting lamps b_i within a predetermined number of times (eg, 5 times), a suboptimal position is selected. The suboptimal position may be a coordinate position where the local conflict degree fails to decrease after movement.

[0117] Based on the above operation of re - determining the coordinates for the conflicting lamp set B, set the preset iteration count (e.g., 50 times) and the preset convergence threshold THRESHOLD (e.g., 0.01) to be repeatedly executed; and calculate the initial overall conflict degree C = Σ C(b_i), where b_i ∈ B, and repeatedly execute the following steps:

[0118] a) Execute the process of the above steps S502 - S507 for each lamp in the conflicting lamp set B;

[0119] b) Calculate the new overall conflict degree C_new;

[0120] c) If (C - C_new) / C < THRESHOLD, exit the iterative loop;

[0121] d) If (C - C_new) / C ≥ THRESHOLD, update C = C_new and continue the next iterative operation; until the iteration count reaches the preset iteration count or the convergence value reaches the preset convergence threshold.

[0122] Based on the process of the above steps S501 - S507, it can be ensured that different lamps do not occupy the same coordinates, which also corresponds to the fact that the actually installed lamps do not overlap in the actual space.

[0123] In some embodiments, for special situations caused by multi - floor or special building shapes, it can be decomposed by dividing into areas to reduce the adverse effects of conflicting lamps and isolated lamps on the operation of this method.

[0124] In one embodiment, as Figure 7 shown, step S104 includes:

[0125] For each optimized coordinate map, execute the following coordinate transformation process:

[0126] S701. Confirm the triggered order of each lamp in the generated optimized coordinate map;

[0127] S702. Confirm the wireless communication information between each lamp according to the triggered order, and generate the wireless communication information including the time spent and communication speed when each lamp sends and receives wireless communication signals with each other when triggered;

[0128] S703. Calculate the relative distance and relative angular direction between each lamp according to the wireless communication information between each lamp, and obtain the relative position relationship of each lamp in the actual space distribution;

[0129] S704. Perform coordinate transformation processing on each lamp in the optimized coordinate map according to the relative position relationship of each lamp to obtain the optimized coordinate map after coordinate transformation, and the current coordinate transformation process ends.

[0130] By merging all optimized coordinate graphs after executing the coordinate transformation process, the final lamp deployment location map can be generated.

[0131] In this embodiment, in each optimized position map, the order in which each lamp is triggered is determined according to the timestamp when each lamp is triggered, thereby determining the distribution order of each lamp in each optimized coordinate map. For example, in an optimized coordinate map, lamps 1 to 5 are distributed in sequence, and the relative position relationship of lamps 1 to 5 in the actual spatial distribution can be calculated by using the wireless communication information sent and received between lamps 1 to 5 when they are triggered. The relative position relationship includes relative distance and relative angular direction.

[0132] The relative distance is calculated as follows: determine the adjacent pairs of lamps in order of distribution, multiply the time it takes for lamp 1 to send a communication signal to lamp 2 when it is triggered by the communication speed, and obtain the relative distance between lamp 1 and lamp 2; multiply the time it takes for lamp 2 to send a communication signal to lamp 3 when it is triggered by the communication speed, and obtain the relative distance between lamp 2 and lamp 3. And so on, the relative distance between the adjacent pairs of lamps 1 to 5 can be obtained.

[0133] The relative angle direction is calculated as follows: three adjacent lamps are determined in order of distribution. Based on the relative distances between the three adjacent lamps, the relative angle direction between the three adjacent lamps can be confirmed in combination with the three-point positioning method of geographical measurement. Similarly, the relative angle direction between each lamp can be obtained.

[0134] Based on the relative distance and relative angle direction of each lamp in each optimized coordinate map, the coordinate position of each lamp in the actual spatial distribution in each optimized coordinate map can be confirmed, so as to perform coordinate transformation processing to obtain the optimized coordinate map after coordinate transformation (that is, the actual spatial distribution map of each lamp in the local route lamp group); then merge all the optimized coordinate maps after coordinate transformation and generate the final lamp deployment location map. The lamp deployment location map mainly includes: lamp deployment point schematics, adjacent lamp spacing, and lamp equipment address corresponding to each point, etc., which can be easily processed and identified by computers, providing basic data for advanced lighting control and spatial analysis.

[0135] It should be noted that in the above coordinate transformation process, all lamps are on the same plane, that is, there is no influence of factors such as floors and mezzanines, and the data processing is for two-dimensional display. However, this embodiment can realize coordinate data processing applied to multi-floor three-dimensional space by expanding the z coordinate.

[0136] In one embodiment, if Figure 8 As shown, step S105 includes:

[0137] S801. When the building map is an engineering drawing, specify the corresponding positions of two lamps in the building map and bind them. Based on the corresponding position relationship of the two specified lamps, the topological positions of the remaining lamps and the lamp installation positions on the building map are automatically bound by the software;

[0138] S802: When the building map is a graphical schematic map, a contour fitting image processing algorithm is used to bind the building map and the lamp deployment location map accordingly.

[0139] In step S801 of this embodiment, when the building map is an engineering drawing (such as a CAD engineering drawing) with clear building structure, scale and IoT lamp installation location, the corresponding binding method is: specify two lamps at the deployment site for name recognition ID, and identify the corresponding positions of the two specified lamps in the building map in combination with the deployment site environment for binding; based on the corresponding position relationship of the two specified lamps, the software can automatically bind the topological positions of the remaining lamps to the lamp installation positions on the building map. For example Fig.10 A schematic diagram of a scene outline of an embodiment shown and Fig.11 shown Fig.10 The corresponding deployment location topology map under the scenario, where only individual lamps are shown in the deployment location topology map for convenient explanation. The corresponding binding process is: the location of lamp 1a is at the entrance of the building, the operator arrives at the entrance, identifies lamps 1a and 2b on site, and corresponds the device addresses of lamps 1a and 2b to the two corresponding lamp positions at the entrance of the CAD engineering drawing, and then the CAD engineering drawing and the deployment location topology map will automatically correspond to the device addresses of other lamps and their related location data.

[0140] In step S802 of this embodiment, when the building map is a graphical schematic map with a boundary that is similar to the actual building structure outline, the corresponding binding method is: first enter the scale of the graphical schematic map; define reference points in the graphical schematic map and the lamp deployment location map; define the graphical schematic map of the symmetrical outline and the graphical schematic map of the asymmetrical outline of the reference point, and use the contour fitting image processing algorithm to bind the building map and the lamp deployment location map accordingly.

[0141] In some other embodiments of the present invention, steps S102-104 can also be implemented using other algorithms. For example: use the force-directed graph drawing algorithm (Fruchterman-Reingold) to preliminarily arrange the lamp nodes, then apply the multi-dimensional scaling MDS technology to map the high-dimensional data to a two-dimensional or three-dimensional space, and then use the t-SNE algorithm (t-distributed stochastic neighbor embedding) to optimize the local structure. Another example: train a neural network model to learn the mapping from neighbor relationships to spatial positions, use reinforcement learning technology to optimize the relative positions of lamps through repeated trials; then use integer linear programming (ILP) technology to optimize the lamp positions; as new data continues to accumulate, periodically recalculate and optimize the map; and use Kalman filtering technology to smoothly update the lamp position estimate.

[0142] The embodiment of the present invention also provides a device for generating a deployment location map based on an Internet of Things lighting system, and the device for generating a deployment location map based on an Internet of Things lighting system is used to execute any embodiment of the method for generating a deployment location map based on an Internet of Things lighting system. Fig. 9 , Fig. 9 It is a schematic block diagram of a device for generating a deployment location map based on an Internet of Things lighting system provided by an embodiment of the present invention.

[0143] like Fig. 9 As shown, the device 900 for generating a deployment location map based on an IoT lighting system includes: a data acquisition unit 901, an initial coordinate processing unit 902, an optimized coordinate processing unit 903, a map generation unit 904, and a map binding unit 905;

[0144] The data acquisition unit 901 is used to determine the current time period when the current lamp is triggered, obtain other adjacent lamps that are triggered within the current time period and form a current local route lamp group, obtain the time point data when the current local route lamp group is triggered and generate initial data corresponding to the current local route lamp group;

[0145] The initial coordinate processing unit 902 is used to calculate the coordinates of the lamps using the initial data corresponding to each local route lamp group through graph theory and heuristic algorithms to obtain the initial coordinates of each lamp in each local route lamp group and generate an initial coordinate graph corresponding to each local route lamp group;

[0146] The optimized coordinate processing unit 903 is used to optimize the position of the initial coordinates of each lamp in each initial coordinate map by using a hill climbing algorithm to generate an optimized coordinate map corresponding to each local route lamp group;

[0147] The map generation unit 904 is used to convert the relative position relationship of each lamp in the actual spatial distribution according to the wireless communication information sent and received between each lamp in each local route lamp group when it is triggered, and transform the lamp coordinates in the corresponding optimized coordinate map using the relative position relationship to obtain the optimized coordinate map after the coordinate transformation, and merge the optimized coordinate maps after the coordinate transformation corresponding to all local route lamp groups to generate a lamp deployment location map;

[0148] The map binding unit 905 is used to bind the lamp deployment location map with the building map where the IoT lamp system is located.

[0149] In the device, the initial data of each local route lighting group is obtained; the initial data of each local route lighting group is used through graph theory and heuristic algorithms to generate an initial coordinate map corresponding to each local route lighting group; and the lighting positions of each initial coordinate map are iteratively optimized multiple times through a hill climbing algorithm to generate an optimized coordinate map corresponding to each local route lighting group, and then each optimized coordinate map is adjusted to the overall coordinate system to form a lighting deployment position map representing the distribution of lighting fixtures in the space, and the lighting deployment position map is bound to the building map. This embodiment reduces the work of manual measurement and has the advantages of high automation efficiency and low deployment cost. It can also automatically adapt to the addition, removal or repositioning of lighting fixtures, and can continuously improve map accuracy under long-term data accumulation. Provide basic data for advanced lighting control and spatial analysis.

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0151] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for generating a deployment location map based on an Internet of Things lighting system, characterized in that: include: Determine the current time period when the current lamp is triggered, obtain other adjacent lamps triggered within the current time period and form a current local route lamp group, obtain the time point data when the current local route lamp group is triggered and generate initial data corresponding to the current local route lamp group; The initial data corresponding to each local route lamp group is used to calculate the lamp coordinates through graph theory and heuristic algorithms to obtain the initial coordinates of each lamp in each local route lamp group, and generate an initial coordinate graph corresponding to each local route lamp group; The hill climbing algorithm is used to optimize the position of the initial coordinates of each lamp in each initial coordinate map, and an optimized coordinate map corresponding to each local route lamp group is generated; The relative position relationship of each lamp in the actual spatial distribution is calculated based on the wireless communication information sent and received between the lamps in each local route lamp group when they are triggered, and the lamp coordinates in the corresponding optimized coordinate diagram are transformed using the relative position relationship to obtain the optimized coordinate diagram after the coordinate transformation, and the optimized coordinate diagrams after the coordinate transformation corresponding to all local route lamp groups are merged to generate a lamp deployment location map; Binding the lamp deployment location map with the building map where the IoT lamp system is located; Among them, the lamp coordinate calculation process is performed using the initial data corresponding to each local route lamp group through graph theory and heuristic algorithms to obtain the initial coordinates of each lamp in each local route lamp group, and an initial coordinate diagram corresponding to each local route lamp group is generated, including: for each local route lamp group, the following coordinate calculation process is executed: the lamp with the most neighboring lamps in the local route lamp group is used as the origin lamp; the origin lamp is set as the origin and a coordinate axis is established; the two lamps farthest apart from all the neighboring lamps of the origin lamp are selected as the farthest lamps, and the two farthest lamps are placed at both ends of the X-axis of the coordinate axis; the neighbor relationship between the remaining other neighboring lamps and the two farthest lamps is confirmed, and the other neighboring lamps that have a neighbor relationship with the corresponding farthest lamp are placed at integer coordinate positions adjacent to the corresponding farthest lamp in the coordinate axis to generate the initial coordinate diagram of the local route lamp group; the coordinate calculation process is repeated to complete the coordinate calculation for each local route lamp group, and an initial coordinate diagram corresponding to each local route lamp group is generated.

2. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 1, characterized in that: The method of determining the current time period when the current lamp is triggered, obtaining other adjacent lamps triggered within the current time period and forming a current local route lamp group, obtaining the time point data when the current local route lamp group is triggered and generating initial data corresponding to the current local route lamp group includes: When the current lamp is triggered by the detected object, the current timestamp of the triggering is recorded and a triggered wireless communication signal is sent to the surrounding lamps; Select the previous preset time period and the next preset time period of the current timestamp and merge them into the current time period; Get other lamps triggered in the current time period and use them as neighboring lamps of the current lamp to form the current local route lamp group; Obtain the time point data when each lamp in the current local route lamp group is triggered and generate initial data corresponding to the current local route lamp group, wherein the time point data includes: the timestamp when each lamp is triggered, the device address of each lamp, and the wireless communication information sent and received between each lamp.

3. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 1, characterized in that: The coordinate calculation process also includes: When there is an unplaced lamp that has no neighbor relationship with the two farthest lamps among the remaining neighboring lamps, all placed lamps that have a neighbor relationship with the unplaced lamp are obtained; When there are multiple placed lamps, calculate the average coordinates of all the placed lamps, and place the unplaced lamps at the integer coordinate position closest to the average coordinate in the coordinate axis; When there is one placed lamp, the unplaced lamps are placed at adjacent integer coordinate positions around the placed lamps in the coordinate axis.

4. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 1, characterized in that: The method of using a hill climbing algorithm to optimize the position of the initial coordinates of each lamp in each initial coordinate map to generate an optimized coordinate map corresponding to each local route lamp group includes: For each placed luminaire in each initial coordinate map, perform the following iterative optimization process: Calculate the average coordinates of all neighboring lamps of the placed lamp; Calculating the Manhattan distance between the coordinate value of the placed lamp and the average value of the coordinate to obtain a first Manhattan distance; Calculating the Manhattan distances between a plurality of adjacent integer coordinate values ​​around the placed lamp and the coordinate average value, respectively, to obtain a plurality of second Manhattan distances; When at least one of the plurality of second Manhattan distances is smaller than the first Manhattan distance and the corresponding adjacent integer coordinates are not occupied, moving the placed lamp to the corresponding adjacent integer coordinates that are not occupied; Calculate the overall layout scores of the placed lamps before and after they are moved to unoccupied adjacent integer coordinates respectively, and if the overall layout score after the movement is greater than the overall layout score before the movement, cancel the movement operation of the placed lamps; The iterative optimization process is repeatedly executed until the optimization is completed, and an optimization coordinate diagram corresponding to each local route lamp group is generated.

5. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 4, characterized in that: The iterative optimization process also includes: When the placed lamp is an isolated lamp without adjacent lamps, the placed lamp is placed at the integer coordinate farthest from all the placed lamps in the coordinate axis.

6. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 4, characterized in that: The iterative optimization process also includes: When multiple placed luminaires have the same placement coordinates, they are constructed into a conflicting luminaire set and the following coordinate redetermination process is performed: Compare the ID size of each conflicting lamp in the conflicting lamp set, retain the conflicting lamp with the smallest ID in the conflicting coordinates, and update the conflicting lamp set; Obtaining an updated neighboring lamp set of each conflicting lamp in the conflicting lamp set; Calculate the distance relationship between each conflicting lamp and each adjacent lamp in its adjacent lamp set; Calculate the deviation direction of each conflicting luminaire in its neighboring luminaire set; According to the distance relationship and the deviation direction, a new position is searched by step length starting from the current conflicting coordinates to find at least one unoccupied candidate coordinate position; Calculating the change in the local conflict degree of each conflicting lamp in the conflicting lamp set after it moves from the current conflicting coordinate to each of the candidate coordinate positions, and moving the conflicting lamp to the candidate coordinate position where the local conflict degree can be reduced; When multiple conflicting lamps select the same candidate coordinate position, the conflicting lamp with the smallest ID among the multiple conflicting lamps is retained in the candidate coordinate position, and the new position is searched in steps with the candidate coordinate position as the current starting point until the coordinates of all conflicting lamps are determined.

7. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 6, characterized in that: The relative position relationship of each lamp in the actual spatial distribution is calculated based on the wireless communication information sent and received between each lamp in each local route lamp group when it is triggered, and the lamp coordinates in the corresponding optimized coordinate map are transformed using the relative position relationship to obtain the optimized coordinate map after the coordinate transformation, and the optimized coordinate maps after the coordinate transformation corresponding to all local route lamp groups are merged to generate a lamp deployment location map, including: For each optimized coordinate graph, the following coordinate transformation process is performed: Confirm the triggering order of each lamp in the optimization coordinate diagram; Confirming wireless communication information between the lamps in the order of being triggered, wherein the wireless communication information includes the time and communication speed of sending and receiving wireless communication signals between the lamps when they are triggered; According to the wireless communication information between the lamps, the relative distance and relative angle direction between the lamps are calculated to obtain the relative position relationship of the lamps in the actual spatial distribution; According to the relative position relationship of each lamp, coordinate transformation processing is performed on each lamp in the optimization coordinate diagram to obtain the optimized coordinate diagram after coordinate transformation, and the current coordinate transformation process ends; Merge all optimized coordinate maps after executing the coordinate transformation process to generate the final lamp deployment location map.

8. The method for generating a deployment location map based on an Internet of Things lighting system according to claim 1, characterized in that: The step of binding the lamp deployment location map with the building map where the IoT lamp system is located includes: When the building map is an engineering drawing, the corresponding positions of two lamps in the building map are specified and bound, and based on the corresponding position relationship of the two specified lamps, the topological positions of the remaining lamps and the lamp installation positions on the building map are automatically bound by the software; When the building map is a graphical schematic map, a contour fitting image processing algorithm is used to bind the building map and the lamp deployment location map accordingly.

9. A device for generating a deployment location map based on an Internet of Things lighting system, characterized in that: include: A data acquisition unit, used to determine the current time period when the current lamp is triggered, acquire other adjacent lamps triggered within the current time period and form a current local route lamp group, acquire the time point data when the current local route lamp group is triggered and generate initial data corresponding to the current local route lamp group; An initial coordinate processing unit, used to calculate and process the coordinates of the lamps using the initial data corresponding to each local route lamp group through graph theory and heuristic algorithms, obtain the initial coordinates of each lamp in each local route lamp group, and generate an initial coordinate graph corresponding to each local route lamp group; An optimized coordinate processing unit, used for performing position optimization processing on the initial coordinates of each lamp in each initial coordinate map by using a hill climbing algorithm, and generating an optimized coordinate map corresponding to each local route lamp group; A map generation unit is used to convert the relative position relationship of each lamp in the actual spatial distribution according to the wireless communication information sent and received between each lamp in each local route lamp group when it is triggered, and transform the lamp coordinates in the corresponding optimized coordinate map using the relative position relationship to obtain the optimized coordinate map after the coordinate transformation, and merge the optimized coordinate maps after the coordinate transformation corresponding to all local route lamp groups to generate a lamp deployment location map; A map binding unit, used to bind the lamp deployment location map with the building map where the IoT lamp system is located; Among them, the initial coordinate processing unit is used to execute the following coordinate calculation process for each local route lamp group: the lamp with the most neighboring lamps in the local route lamp group is used as the origin lamp; the origin lamp is set as the origin and a coordinate axis is established; the two lamps farthest apart from all the neighboring lamps of the origin lamp are selected as the farthest lamps, and the two farthest lamps are placed at both ends of the X-axis of the coordinate axis; the neighboring relationship between the remaining other neighboring lamps and the two farthest lamps is confirmed, and the other neighboring lamps that have a neighboring relationship with the corresponding farthest lamp are placed at the integer coordinate positions of the corresponding farthest lamp in the coordinate axis to generate the initial coordinate map of the local route lamp group; the coordinate calculation process is repeated to complete the coordinate calculation for each local route lamp group, and an initial coordinate map corresponding to each local route lamp group is generated.

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