Modular temporary site based office intelligent control system and method

Abstract

The application discloses an office intelligent control system and method based on a modular temporary site, relates to the technical field of intelligent control of temporary office sites, and is based on a plurality of detachable cabins to construct a temporary office site, deploys an intelligent control system to control a plurality of devices in the temporary office site, reads a total amount of network channels of the temporary office site, determines a control difference value based on online channels, determines whether an instruction can normally operate based on the control difference value, and feeds back a warning signal to an administrator end if the intelligent control system feeds back that the instruction cannot normally operate. In the deployment and optimization process of the temporary office system, the application solves the problem that a traditional routing device appears network fluctuation or collapse when facing a plurality of intelligent terminals concurrent connection and complex business scene linkage, and creates an intelligent control system integrating intelligent judgment and network management functions based on a temporary office scene.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for temporary office spaces, specifically an intelligent control system and method for office spaces based on modular temporary spaces. Background Technology

[0002] In scenarios such as emergency rescue, field operations, and temporary offices for large-scale events, existing container-style temporary buildings are mostly single-unit structures, which are difficult to meet the diverse needs of integrated office work. Furthermore, equipment control relies on manual operation, lacking a unified intelligent management system. A difficult-to-reconcile issue has always existed between temporary office spaces and intelligent management systems. The core of an intelligent system is a stable local area network, but the networks in temporary office spaces are inherently unstable, or the routers have insufficient performance, making it difficult to support concurrent connections of multiple intelligent devices. Meanwhile, in the multi-scenario processing or linkage of intelligent management systems, network crashes and interruptions frequently occur. Therefore, how to handle multi-scenario intelligent linkage in temporary office spaces is currently one of the most challenging problems to solve. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent control system and method for office spaces based on modular temporary spaces, in order to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent office control method based on modular temporary spaces, the method comprising:

[0005] A temporary office space is constructed based on several detachable cabins, and an intelligent control system is deployed to control several devices in the temporary office space.

[0006] The intelligent control system reads the total number of network channels in the temporary office space, determines the control difference based on the online channels, and determines whether the command can operate normally based on the control difference;

[0007] The instructions refer to scenario-based instructions issued by the administrator, including scenario execution and scenario linkage; scenario execution refers to the administrator requesting the intelligent control system to execute a certain scenario, and scenario linkage refers to the administrator requesting the intelligent control system to execute multiple scenarios concurrently;

[0008] If the intelligent control system fails to function properly, it will generate an early warning signal and send it to the administrator.

[0009] According to the above technical solution, the several detachable cabins are rectangular cabins, and several devices are distributed in each cabin. After the temporary office space is formed, the administrator uploads the three-dimensional model of the temporary office space to the intelligent control system.

[0010] According to the above technical solution, the control difference based on online channel determination refers to determining the maximum number of channels that can be supported after the network is set up in the temporary office space. The intelligent control system reads the real-time number of channels participating in the network during office work, and solves the control difference based on the difference between the maximum number of channels and the real-time number of channels, which is defined as the total number of channels that can serve subsequent scenarios.

[0011] Each channel serves one device.

[0012] According to the above technical solution, determining whether the instruction can operate normally based on the control difference includes:

[0013] If the instruction is for scenario execution, retrieve the devices associated with the scenario. If the devices associated with the scenario overlap with the devices involved in real-time office work, proceed to scenario linkage processing. If the devices associated with the scenario do not overlap with the devices involved in office work, and the sum of the number of devices associated with the scenario and the number of devices involved in office work exceeds the maximum number of channels, then directly report that the instruction cannot run normally. If the devices associated with the scenario do not overlap with the devices involved in office work, and the sum of the number of devices associated with the scenario and the number of devices involved in office work does not exceed the maximum number of channels, then the scenario instruction is executed normally.

[0014] According to the above technical solution, the scene linkage processing includes:

[0015] When it is determined that the devices associated with a scenario overlap with the devices involved in real-time office work, or when the administrator simultaneously issues and executes multiple scenarios, scenario linkage processing is formed.

[0016] In scene linkage processing, the total number of participating devices is determined first. If the total number of participating devices is higher than the maximum number of channels, the command is directly fed back as not being able to operate normally.

[0017] If the total number of participating devices is not higher than the maximum number of channels, retrieve the overlapping devices and perform further analysis on them;

[0018] The further analysis includes determining whether overlapping devices can share a channel. As mentioned above, one channel serves one device, and overlapping devices are the same device. Here, the analysis mainly focuses on whether two or more instructions can be transmitted on the same channel.

[0019] According to the above technical solution, determining whether overlapping devices can share a channel includes:

[0020] A training model is built based on test data. The test data uses the same rectangular cabin for data transmission testing to determine the transmission data, specifically including transmission distance, transmission obstacles, and the number of transmission commands. The transmission distance refers to the straight-line distance in space, and the transmission obstacles refer to the number of cabin panels that the straight-line distance passes through. The test results are obtained by comparing the received data with the source data; if they match, the transmission is considered successful.

[0021] If a data transmission fails, a new channel is added and the test is repeated until the transmission is successful. If the number of channels added is greater than the number of transmission commands and the transmission still fails, the data is defined as abnormal and saved to the abnormal data group.

[0022] According to the above technical solution, it also includes:

[0023] The judgment equation for constructing the training model:

[0024] Select the minimum number of channels required for successful transmission from each test data set to form a dataset. The corresponding data in each dataset set is: transmission distance. Transmission barriers Number of transmission instructions and the minimum number of channels used ;

[0025] Construct a second-order polynomial to express the relational function:

[0026]

[0027]

[0028] in, This represents the minimum number of channels used for prediction under the i-th data item; , , These represent the transmission distance, transmission obstacle, and number of transmission commands for the i-th data item, respectively. , , , , , , , , , These represent the parameters of the model, which are then vectorized into a set of parameter vectors, denoted as D:

[0029]

[0030] Solving by minimizing the mean square error. ;

[0031]

[0032] in, Represents the loss function; This represents the mean squared error, where j refers to the index and N refers to the total number of data points. This represents the minimum number of channels output. The input represents the vectorized transmission distance, transmission obstacles, and number of transmission commands; T refers to the transpose. Represents L2 regularization, Express the regularization strength;

[0033] After solving, the parameter vector is substituted into the second-order polynomial expression function as the model output. Upon receiving the administrator's instruction, a predicted output is generated and rounded up to the nearest integer. This is then compared with the corresponding control difference to determine whether the instruction can be executed normally.

[0034] Since the amount of data behind instructions is relatively average in temporary office spaces, and there is no extremely complex instruction parsing, each instruction is treated as an independent entity in the analysis, and the amount of data represented is relatively average.

[0035] According to the above technical solution, it also includes:

[0036] Handling of abnormal data groups;

[0037] Extract the data with the largest mode from the abnormal data group as the split data. If any data in the input data is greater than the split data, the feedback instruction will not function properly.

[0038] An intelligent control system for modular temporary office spaces, the system comprising:

[0039] The scene instruction module is used to receive scene-based instructions issued by the administrator and forward them to the intelligent control module, including scene execution and scene linkage; the scene execution refers to the administrator requesting the intelligent control system to execute a certain scene, and the scene linkage refers to the administrator requesting the intelligent control system to execute multiple scenes concurrently;

[0040] The intelligent control module is used to read the total number of network channels in the temporary office space, determine the control difference based on the online channels, and determine whether the command can run normally based on the control difference.

[0041] The early warning feedback module is used to generate an early warning signal and send it to the administrator if the intelligent control module fails to operate normally.

[0042] Compared with the prior art, the beneficial effects of the present invention are: In the process of deployment and optimization of temporary office system, this application focuses on the basic stability of network environment, solves the problem of network fluctuation or crash when traditional routing equipment faces multiple intelligent terminal concurrent connection and complex business scenario linkage, and creates an intelligent control system that integrates intelligent judgment and network management functions based on temporary office scenario.

[0043] When multiple intelligent scenarios are executed simultaneously, the stability of the network can be predicted in advance. This stability not only improves the response accuracy of intelligent devices, but also fundamentally ensures the continuity and efficiency of office processes, breaks through the network bottlenecks in temporary office environments, and provides a solid guarantee for the reliable operation of the intelligent office ecosystem. Attached Figure Description

[0044] Figure 1 This is a schematic diagram illustrating the steps of an intelligent office control method based on modular temporary spaces according to the present invention. Detailed Implementation

[0045] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example: Figure 1 As shown, an intelligent office control method based on modular temporary spaces is proposed, which includes:

[0047] A temporary office space is constructed based on several detachable cabins, and an intelligent control system is deployed to control several devices in the temporary office space.

[0048] The intelligent control system reads the total number of network channels in the temporary office space, determines the control difference based on the online channels, and determines whether the command can operate normally based on the control difference;

[0049] The instructions refer to scenario-based instructions issued by the administrator, including scenario execution and scenario linkage; scenario execution refers to the administrator requesting the intelligent control system to execute a certain scenario, and scenario linkage refers to the administrator requesting the intelligent control system to execute multiple scenarios concurrently;

[0050] If the intelligent control system fails to function properly, it will generate an early warning signal and send it to the administrator.

[0051] The detachable compartments are rectangular, and each compartment contains several pieces of equipment. After the temporary office space is formed, the administrator uploads the 3D model of the temporary office space to the intelligent control system.

[0052] In this embodiment, the system comprises four container units, each 7.8m long, 3.2m wide, and 3.3m high, designated as hold 1 (front), hold 2, hold 3, and hold 4 (rear). The containers are connected via external snap-fit ​​connections, with internal pre-installed connectors for quick connection of network cables, lighting cables, RS-485 cables, air conditioning cables, and electrical outlet cables.

[0053] The No. 1 front compartment is equipped with a power distribution box, emergency lights, a 3p cabinet air conditioner, sockets, lights, a high-voltage switch panel, a wireless access point, and a 4-inch 86-type main screen. The power distribution box contains a main leakage protection device, a meter, relays, contactors, circuit breakers, and DC power supply. The No. 4 rear compartment is equipped with a 7-inch screen, lights, a high-voltage panel, a wireless access point, a 3p cabinet air conditioner, sockets, and emergency lights. The No. 2 and No. 3 compartments are equipped with lights and sockets.

[0054] The control difference based on online channels refers to determining the maximum number of channels that can be supported after the network is set up in the temporary office space. The intelligent control system reads the real-time number of channels participating in the network during office work, and solves the control difference based on the difference between the maximum number of channels and the real-time number of channels. It is defined as the total number of channels that can serve subsequent scenarios.

[0055] Each channel serves one device.

[0056] The process of determining whether an instruction can operate normally based on control differences includes:

[0057] If the instruction is for scenario execution, retrieve the devices associated with the scenario. If the devices associated with the scenario overlap with the devices involved in real-time office work, proceed to scenario linkage processing. If the devices associated with the scenario do not overlap with the devices involved in office work, and the sum of the number of devices associated with the scenario and the number of devices involved in office work exceeds the maximum number of channels, then directly report that the instruction cannot run normally. If the devices associated with the scenario do not overlap with the devices involved in office work, and the sum of the number of devices associated with the scenario and the number of devices involved in office work does not exceed the maximum number of channels, then the scenario instruction is executed normally.

[0058] The scene linkage processing includes:

[0059] When it is determined that the devices associated with a scenario overlap with the devices involved in real-time office work, or when the administrator simultaneously issues and executes multiple scenarios, scenario linkage processing is formed.

[0060] In this embodiment, two simple scenarios are used as examples. The first scenario is to turn off all lights with one click when leaving get off work, and the second scenario is to turn on a certain night light when leaving get off work.

[0061] If a night light and a light are set to be controlled by the same switch, then the devices overlap, and two commands will be sent to one device, thus forming scene linkage processing.

[0062] In scene linkage processing, the total number of participating devices is determined first. If the total number of participating devices is higher than the maximum number of channels, the command is directly fed back as not being able to operate normally.

[0063] If the total number of participating devices is not higher than the maximum number of channels, retrieve the overlapping devices and perform further analysis on them;

[0064] The further analysis includes determining whether overlapping devices can share a single channel.

[0065] The determination of whether overlapping devices can share a channel includes:

[0066] A training model is built based on test data. The test data uses the same rectangular cabin for data transmission testing to determine the transmission data, specifically including transmission distance, transmission obstacles, and the number of transmission commands. The transmission distance refers to the straight-line distance in space, and the transmission obstacles refer to the number of cabin panels that the straight-line distance passes through. The test results are obtained by comparing the received data with the source data; if they match, the transmission is considered successful.

[0067] If a data transmission fails, a new channel is added and the test is repeated until the transmission is successful. If the number of channels added is greater than the number of transmission commands and the transmission still fails, the data is defined as abnormal and saved to the abnormal data group.

[0068] Also includes:

[0069] The judgment equation for constructing the training model:

[0070] Select the minimum number of channels required for successful transmission from each test data set to form a dataset. The corresponding data in each dataset set is: transmission distance. Transmission barriers Number of transmission instructions and the minimum number of channels used ;

[0071] Construct a second-order polynomial to express the relational function:

[0072]

[0073]

[0074] in, This represents the minimum number of channels used for prediction under the i-th data item; , , These represent the transmission distance, transmission obstacle, and number of transmission commands for the i-th data item, respectively. , , , , , , , , , These represent the parameters of the model, which are then vectorized into a set of parameter vectors, denoted as D:

[0075]

[0076] Solving by minimizing the mean square error. ;

[0077]

[0078] in, Represents the loss function; This represents the mean squared error, where j refers to the index and N refers to the total number of data points. This represents the minimum number of channels output. The input represents the vectorized transmission distance, transmission obstacles, and number of transmission commands; T refers to the transpose. Represents L2 regularization, Express the regularization strength;

[0079] After solving, the parameter vector is substituted into the second-order polynomial expression function as the model output. Upon receiving the administrator's instruction, a predicted output is generated and rounded up to the nearest integer. This is then compared with the corresponding control difference to determine whether the instruction can be executed normally.

[0080] Also includes:

[0081] Handling of abnormal data groups;

[0082] Extract the data with the largest mode from the abnormal data group as the split data. If any data in the input data is greater than the split data, the feedback instruction will not function properly.

[0083] This application also provides an intelligent office control system based on modular temporary spaces, the system comprising:

[0084] The scene instruction module is used to receive scene-based instructions issued by the administrator and forward them to the intelligent control module, including scene execution and scene linkage; the scene execution refers to the administrator requesting the intelligent control system to execute a certain scene, and the scene linkage refers to the administrator requesting the intelligent control system to execute multiple scenes concurrently;

[0085] The intelligent control module is used to read the total number of network channels in the temporary office space, determine the control difference based on the online channels, and determine whether the command can run normally based on the control difference.

[0086] The early warning feedback module is used to generate an early warning signal and send it to the administrator if the intelligent control module fails to operate normally.

[0087] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent office control method based on modular temporary spaces, characterized in that: The method includes: A temporary office space is constructed based on several detachable cabins, and an intelligent control system is deployed to control several devices in the temporary office space. The intelligent control system reads the total number of network channels in the temporary office space, determines the control difference based on the online channels, and determines whether the command can operate normally based on the control difference; The instructions refer to scenario-based instructions issued by the administrator, including scenario execution and scenario linkage; scenario execution refers to the administrator requesting the intelligent control system to execute a certain scenario, and scenario linkage refers to the administrator requesting the intelligent control system to execute multiple scenarios concurrently; If the intelligent control system fails to function properly, it will generate an early warning signal and send it to the administrator. The control difference based on online channels refers to determining the maximum number of channels that can be supported after the network is set up in the temporary office space. The intelligent control system reads the real-time number of channels participating in the network during office work, and solves the control difference based on the difference between the maximum number of channels and the real-time number of channels. It is defined as the total number of channels that can serve subsequent scenarios. The process of determining whether an instruction can operate normally based on control differences includes: If the instruction is for scenario execution, retrieve the devices associated with the scenario. If the devices associated with the scenario overlap with the devices involved in real-time office work, proceed to scenario linkage processing. If the devices associated with the scenario do not overlap with the devices involved in office work, and the sum of the number of devices associated with the scenario and the number of devices involved in office work exceeds the maximum number of channels, then directly report that the instruction cannot run normally. If the devices associated with the scenario do not overlap with the devices involved in office work, and the sum of the number of devices associated with the scenario and the number of devices involved in office work does not exceed the maximum number of channels, then the scenario instruction is executed normally.

2. The intelligent office control method based on modular temporary spaces according to claim 1, characterized in that: The detachable compartments are rectangular, and each compartment contains several pieces of equipment. After the temporary office space is formed, the administrator uploads the 3D model of the temporary office space to the intelligent control system.

3. The intelligent office control method based on modular temporary spaces according to claim 1, characterized in that: Each channel serves one device.

4. The intelligent office control method based on modular temporary spaces according to claim 1, characterized in that: The scene linkage processing includes: When it is determined that the devices associated with a scenario overlap with the devices involved in real-time office work, or when the administrator simultaneously issues and executes multiple scenarios, scenario linkage processing is formed. In scene linkage processing, the total number of participating devices is determined first. If the total number of participating devices is higher than the maximum number of channels, the command is directly fed back as not being able to operate normally. If the total number of participating devices is not higher than the maximum number of channels, retrieve the overlapping devices and perform further analysis on them; The further analysis includes determining whether overlapping devices can share a single channel.

5. The intelligent office control method based on modular temporary spaces according to claim 4, characterized in that: The determination of whether overlapping devices can share a channel includes: A training model is built based on test data. The test data uses the same rectangular cabin for data transmission testing to determine the transmission data, specifically including transmission distance, transmission obstacles, and the number of transmission commands. The transmission distance refers to the straight-line distance in space, and the transmission obstacles refer to the number of cabin panels that the straight-line distance passes through. The test results are obtained by comparing the received data with the source data; if they match, the transmission is considered successful. If a data transmission fails, a new channel is added and the test is repeated until the transmission is successful. If the number of channels added is greater than the number of transmission commands and the transmission still fails, the data is defined as abnormal and saved to the abnormal data group.

6. The intelligent office control method based on modular temporary spaces according to claim 5, characterized in that: Also includes: The judgment equation for constructing the training model: Select the minimum number of channels required for successful transmission from each test data set to form a dataset. The corresponding data in each dataset set is: transmission distance. Transmission barriers Number of transmission instructions and the minimum number of channels used ; Construct a second-order polynomial to express the relational function: in, This represents the minimum number of channels used for prediction under the i-th data item; , , These represent the transmission distance, transmission obstacle, and number of transmission commands for the i-th data item, respectively. , , , , , , , , , These represent the parameters of the model, which are then vectorized into a set of parameter vectors, denoted as D: Solving by minimizing the mean square error. ; in, Represents the loss function; This represents the mean squared error, where j refers to the index and N refers to the total number of data points. This represents the minimum number of channels output. The input represents the vectorized transmission distance, transmission obstacles, and number of transmission commands; T refers to the transpose. Represents L2 regularization, Express the regularization strength; After solving, the parameter vector is substituted into the second-order polynomial expression function as the model output. Upon receiving the administrator's instruction, a predicted output is generated and rounded up to the nearest integer. This is then compared with the corresponding control difference to determine whether the instruction can be executed normally.

7. The intelligent office control method based on modular temporary spaces according to claim 5, characterized in that: Also includes: Handling of abnormal data groups; Extract the data with the largest mode from the abnormal data group as the split data. If any data in the input data is greater than the split data, the feedback instruction will not function properly.

8. An intelligent office control system based on modular temporary spaces, used to implement the intelligent office control method based on modular temporary spaces as described in claim 1, characterized in that: The system includes: The scene instruction module is used to receive scene-based instructions issued by the administrator and forward them to the intelligent control module, including scene execution and scene linkage; the scene execution refers to the administrator requesting the intelligent control system to execute a certain scene, and the scene linkage refers to the administrator requesting the intelligent control system to execute multiple scenes concurrently; The intelligent control module is used to read the total number of network channels in the temporary office space, determine the control difference based on the online channels, and determine whether the command can run normally based on the control difference. The early warning feedback module is used to generate an early warning signal and send it to the administrator if the intelligent control module fails to operate normally.

Images