Simulation method and device for data fusion and dimension reduction between sensor network devices

Abstract

The present invention discloses a simulation method and device for data fusion and dimensionality reduction between sensor network devices. The method includes: obtaining the current load generation object type and generating a return simulation data set by it and a simulation generation strategy, and sending it to a simulation data queue; obtaining a simulation load data set from a simulation data queue based on a simulation load loading strategy, and sending it to a simulation load generation object; the simulation load generation object obtains a target data fusion dimensionality reduction strategy and a target data receiving object corresponding to the current load generation object type, and performs corresponding processing on the simulation load data set based on the target data fusion dimensionality reduction strategy to obtain a simulation processing result and a simulation processing process performance parameter. The embodiment of the present invention integrates a simulation fusion computing gateway into a sensor acquisition subnetwork, and the simulation fusion computing gateway can directly perform simulation stress tests in various scenarios in a sensor operating environment, thereby improving the effectiveness and efficiency of the simulation stress test.

Description

Technical Field

[0001] The present invention relates to the technical field of gateway simulation testing of sensor networks, and in particular to a simulation method and device for data fusion and dimensionality reduction between sensor network devices. Background Art

[0002] At present, the common stress testing software in sensor networks cannot be run in the sensor operating environment due to its limited operating environment, and cannot simulate sensor return data, so it is impossible to test the aggregation gateway in the sensor network. Summary of the invention

[0003] The embodiments of the present invention provide a simulation method and apparatus for data fusion and dimensionality reduction between sensor network devices, aiming to solve the problem that the common stress testing software in sensor networks in the prior art cannot simulate sensor return data due to the limited operating environment, and therefore cannot test the aggregation gateway in the sensor network.

[0004] In a first aspect, an embodiment of the present invention provides a simulation method for data fusion and dimensionality reduction between sensor network devices, which is applied to a simulation fusion computing gateway, wherein the simulation fusion computing gateway is communicatively connected to a cloud server and a convergence gateway, and the convergence gateway is communicatively connected to a sensor acquisition subnet; the simulation method for data fusion and dimensionality reduction between sensor network devices includes:

[0005] In response to the simulation instruction, the current load generation object type is obtained, and a return simulation data set is generated based on the current load generation object type and a preset simulation generation strategy; wherein the current load generation object type is one of the load generation object types in the load generation object type set, and the load generation object type set includes at least a cloud server type and an aggregation gateway type;

[0006] Sending the returned simulation data set to a simulation data queue;

[0007] Acquire a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and send the simulation load data set to a simulation load generation object;

[0008] The simulation load generation object obtains the target data fusion dimensionality reduction strategy and the target data receiving object corresponding to the current load generation object type, processes the simulation load data set based on the target data fusion dimensionality reduction strategy to obtain the simulation processing results and simulation processing performance parameters, and sends them to the target data receiving object.

[0009] In a second aspect, an embodiment of the present invention further provides a simulation device for data fusion and dimensionality reduction between sensor network devices, which is configured in a simulation fusion computing gateway, wherein the simulation fusion computing gateway is communicatively connected to a cloud server and a convergence gateway, and the convergence gateway is communicatively connected to a sensor acquisition subnet; the simulation device for data fusion and dimensionality reduction between sensor network devices includes:

[0010] A simulation data generation unit, configured to obtain the current load generation object type in response to a simulation instruction, and generate a return simulation data set based on the current load generation object type and a preset simulation generation strategy; wherein the current load generation object type is one of the load generation object types in the load generation object type set, and the load generation object type set includes at least a cloud server type and an aggregation gateway type;

[0011] A simulation data queue unit, sending the returned simulation data set to a simulation data queue;

[0012] A simulation load loading unit, configured to obtain a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and send the simulation load data set to a simulation load generation object;

[0013] The simulation load generation unit is used for the simulation load generation object to obtain the target data fusion and dimensionality reduction strategy and the target data receiving object corresponding to the current load generation object type, and to process the simulation load data set accordingly based on the target data fusion and dimensionality reduction strategy to obtain the simulation processing results and simulation processing performance parameters, and send them to the target data receiving object.

[0014] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method described in the first aspect above can be implemented.

[0016] The embodiment of the present invention provides a simulation method and device for data fusion and dimensionality reduction between sensor network devices, which is applied to a simulation fusion computing gateway, wherein the simulation fusion computing gateway is communicatively connected to a cloud server and a convergence gateway, and the convergence gateway is communicatively connected to a sensor acquisition subnet; the method comprises: in response to a simulation instruction, obtaining the current load generation object type, and generating a return simulation data set based on the current load generation object type and a preset simulation generation strategy; wherein the current load generation object type is one of the load generation object types in a load generation object type set, and the load generation object type set includes at least a cloud server type and a convergence gateway type; sending the return simulation data set to a simulation data queue; obtaining a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and sending the simulation load data set to the simulation load generation object; the simulation load generation object obtains a target data fusion dimensionality reduction strategy and a target data receiving object corresponding to the current load generation object type, and performs corresponding processing on the simulation load data set based on the target data fusion dimensionality reduction strategy to obtain a simulation processing result and a simulation processing process performance parameter, and sends the result to the target data receiving object. The embodiment of the present invention integrates the simulation fusion computing gateway into the sensor acquisition sub-network, and the simulation fusion computing gateway can directly perform simulation stress testing in various scenarios in the sensor operating environment, thereby improving the effectiveness and efficiency of the simulation stress testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of an application scenario of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a flow chart of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of a sub-process of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of a sub-process of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0022] Figure 5A schematic diagram of a sub-process of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0023] Figure 6 A schematic diagram of a sub-process of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0024] Figure 7 A schematic diagram of a sub-process of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0025] Figure 8 A schematic block diagram of a simulation device for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention;

[0026] Fig. 9 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] 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.

[0029] 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, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0030] 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.

[0031] Please also refer to Figure 1 and Figure 2 ,in Figure 1 Schematic diagram of a scenario of a simulation method for data fusion and dimensionality reduction between sensor network devices according to an embodiment of the present invention. Figure 2FIG. 1 is a flow chart of a simulation method for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention. Figure 1 As shown, the simulation method for data fusion and dimension reduction between sensor network devices provided in the embodiment of the present invention is applied to the simulation fusion computing gateway 10 (which can be understood as an edge server, edge gateway, etc.), and the simulation fusion computing gateway 10 is connected to the cloud server 20 and the convergence gateway 30 (which can also be understood as an edge server, edge gateway, etc.), and the convergence gateway 30 is connected to the sensor acquisition subnet 40. Of course, the convergence gateway 30 can also be connected to the cloud server 20.

[0032] like Figure 2 As shown, the method includes the following steps S110-S140.

[0033] S110. In response to a simulation instruction, obtain a current load generation object type, and generate a feedback simulation data set based on the current load generation object type and a preset simulation generation strategy.

[0034] The current load generating object type is one of the load generating object types in the load generating object type set, and the load generating object type set includes at least a cloud server type and an aggregation gateway type.

[0035] In this embodiment, the technical solution is described with the simulation fusion computing gateway as the execution subject. Figure 1 As shown, the simulation fusion computing gateway 10 can be in the same sensor acquisition subnetwork (and the complete sensor acquisition network includes multiple sensor acquisition subnetworks) with the convergence gateway 30 and the sensor acquisition subnetwork 40 (the sensor acquisition subnetwork 40 includes multiple sensor acquisition nodes, each of which is deployed on a user terminal device, such as a desktop computer, a laptop computer, a local server, etc.). Through the above system architecture, the simulation fusion computing gateway is integrated into the sensor acquisition subnetwork, and the simulation fusion computing gateway can directly perform simulation stress testing in the sensor operating environment.

[0036] Specifically, the simulation fusion computing gateway first performs real-time detection on the simulation instruction. When the simulation instruction generated by the test start virtual button on the user interaction interface of the manual operation simulation fusion computing gateway is detected, or when the simulation instruction generated based on the preset timing test strategy in the detection simulation fusion computing gateway is detected, it is necessary to further obtain the current load generation object type. Because the simulation fusion computing gateway can simulate the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet, and can also simulate the data generated by the convergence gateway after the sensor node data generated by multiple sensor acquisition nodes are aggregated and reduced in dimension, the current load generation object of the simulation fusion computing gateway is specifically one of the load generation object types in the load generation object type set, and the load generation object type set includes at least a cloud server type and an aggregation gateway type. For example, when the simulation fusion computing gateway simulates the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet, the corresponding current load generation object type is the aggregation gateway type; when the simulation fusion computing gateway simulates the data generated by the convergence gateway after the sensor node data generated by multiple sensor acquisition nodes are aggregated and reduced in dimension, the corresponding current load generation object type is the cloud server type.

[0037] After determining the current load generation object type of this simulation test, the simulation data generator (which can also be regarded as a simulation data generation unit) in the simulation fusion computing gateway can be combined with the simulation generation strategy to generate a feedback simulation data set for subsequent simulation tests.

[0038] In one embodiment, as a first embodiment of generating a return simulation data set, as Figure 3 As shown, step S110 includes:

[0039] S111A: If it is determined that the current load generating object type is a convergence gateway type, obtain service logic information corresponding to the sensor acquisition subnet from the convergence gateway;

[0040] S112A. Based on the simulation generation strategy and the multiple business logic types and business logic sequences included in the business logic information, generate the return simulation data set corresponding to the business logic information.

[0041] In this embodiment, if it is determined that the current load generation object type is a convergence gateway type, it means that the simulation fusion computing gateway is simulating the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet. As a first embodiment of generating a return simulation data set, it can be to generate multiple return simulation data in a manner that follows business logic information to generate close to real data, and form the return simulation data set.

[0042] Specifically, first parse and obtain the multiple business logic types and business logic sequences included in the business logic information; then obtain multiple initial simulation data; then process the multiple initial simulation data based on the multiple business logic types, business logic sequences and specific calculation methods corresponding to each business logic in the business logic information to obtain multiple return simulation data; finally, the return simulation data set is composed of the multiple return simulation data obtained above. It can be seen that based on the above method, a large amount of return simulation data for simulation testing can be quickly generated, thereby forming a return simulation data set.

[0043] In one embodiment, as a second embodiment of generating a return simulation data set, as Figure 4 As shown, step S110 includes:

[0044] S111B, if it is determined that the current load generation object type is a convergence gateway type or a cloud server type, obtain a historical business data set corresponding to the sensor acquisition subnet from the convergence gateway; wherein the historical business data set includes multiple business scenario data subsets, each business scenario data subset corresponds to a unique business scenario in the sensor acquisition subnet;

[0045] S112B, amplify and replay the multiple business scenario data subsets included in the historical business data set based on the simulation generation strategy to obtain processed data subsets corresponding to each business scenario data subset, and form the return simulation data set.

[0046] In this embodiment, if it is determined that the current load generation object type is a convergence gateway type, it means that the simulation fusion computing gateway is simulating the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet; if it is determined that the current load generation object type is a cloud server type, it means that the simulation fusion computing gateway is simulating the convergence gateway to aggregate and reduce the sensor node data generated by multiple sensor acquisition nodes. As a second embodiment of generating a return simulation data set, it can be to generate multiple return simulation data by amplifying and replaying historical data, and form the return simulation data set.

[0047] If the simulation fusion computing gateway is used to simulate the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet, the specific method is to first obtain the historical business data set corresponding to the sensor acquisition subnet from the convergence gateway; wherein the historical business data set includes multiple business scenario data subsets, and each business scenario data subset corresponds to a unique business scenario in the sensor acquisition subnet. For example, the business scenarios included in the sensor acquisition subnet at least include the addition of sensor nodes, sensor node offline, sensor failure abnormal offline, sensor data volume surge, sensor acquisition data large-scale abnormality, and sensor subnet increase. Moreover, the historical business data set in this application takes into account the business scenario data subsets for multiple business scenarios in the sensor acquisition subnet. The purpose is to address the problems that the entire sensor acquisition network may encounter in real operation under different business scenarios in the real production environment, so as to adjust the operating parameters of the equipment deployed by the key category sensor acquisition nodes and achieve structural operation optimization.

[0048] Then, historical business data of a preset data volume are selected from all business scenario data subsets or part of the business scenario data subsets in the multiple business scenario data subsets included in the historical business data set. For example, the historical business data set includes N1 business scenario data subsets (N1 is a positive integer), and they are respectively recorded as the 1st business scenario data subset to the N1th business scenario data subset, then the 1st business scenario data subset (for example, corresponding to the business scenario of adding a sensor node) and the 2nd business scenario data subset (for example, corresponding to the business scenario of the sensor node going offline) are first selected, and then N2 historical business data are selected from the 1st business scenario data subset (N2 represents the preset data volume, N2 is a positive integer, and N2 is less than the 1st business scenario data subset). The total number of historical business data in the first business scenario data subset), and N2 historical business data are selected from the second business scenario data subset (N2 is less than the total number of historical business data in the second business scenario data subset), and finally N2 historical business data are selected from the first business scenario data subset and N2 historical business data are selected from the second business scenario data subset to amplify and replay the historical data based on the simulation generation strategy (data amplification can be understood as expanding the data scale of small-scale data based on a specified data amplification strategy), thereby obtaining the first processed data subset corresponding to the N2 historical business data in the first business scenario data subset, and the second processed data subset corresponding to the N2 historical business data in the second business scenario data subset. Finally, the multiple processed data subsets obtained above constitute the return simulation data set. It can be seen that based on the above method, a large number of processed data subsets for simulation testing can be quickly generated to constitute a return simulation data set.

[0049] S120: Send the returned simulation data set to a simulation data queue.

[0050] In this embodiment, when the simulation fusion computing gateway completes the acquisition of the returned simulation data set, it can be first stored in the simulation data queue, which is a load data queue, so that subsequent simulation tests can load and obtain the returned simulation data set at high speed from the simulation data queue.

[0051] S130 . Acquire a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and send the simulation load data set to a simulation load generation object.

[0052] In this embodiment, when the simulation test is specifically started, the simulation data load loader in the simulation fusion computing gateway may first call the simulation load loading strategy to obtain the simulation load data set from the simulation data queue, and then send the simulation load data set to the simulation load generation object. Among them, the simulation load generation object corresponds to the current load generation object type, that is, when the current load generation object type is a convergence gateway type, the simulation load generation object is a convergence gateway object; when the current load generation object type is a cloud server type, the simulation load generation object is a cloud server object. It can be seen that based on the above method, the simulation load data set can be obtained from the simulation data queue based on the simulation load generation object in the simulation fusion computing gateway.

[0053] In one embodiment, if Figure 5 As shown, step S130 includes:

[0054] S131. If it is determined that the current load occurrence object type is a converged gateway type or a cloud server type, obtain the current test stress scenario corresponding to the converged gateway type or the cloud server type based on the simulated load loading strategy, and determine the current data acquisition amount corresponding to the current test stress scenario;

[0055] S132. Acquire a corresponding amount of simulation load data from the simulation data queue based on the current data acquisition amount to compose and update the simulation load data set, and send the simulation load data set to the simulation load generation object.

[0056] In this embodiment, if it is determined that the current load generation object type is a convergence gateway type or a cloud server type, it means that when the current load generation object type is a convergence gateway type, the simulated load generation object is used to simulate multiple sensor acquisition nodes in the sensor acquisition subnet to perform a simulation test on the convergence gateway, or when the current load generation object type is a cloud server type, the simulated load generation object is used to simulate multiple convergence gateways to perform a simulation test on the cloud server. However, no matter whether the final simulation test object of the simulated load generation object is a convergence gateway or a cloud server, the current test pressure scenario can be determined first (such as close to the real production scenario, completely simulated real production scenario, extreme pressure scenario, etc.), and then the current data acquisition amount is determined according to the current test pressure scenario. Finally, after determining the current data acquisition amount, the simulated load data corresponding to the current data acquisition amount is obtained from the simulated load data set in the simulated data queue to form and update the simulated load data set, and the simulated load data set is sent to the simulated load generation object. It can be seen that based on the above method, the simulation load data corresponding to the current data acquisition amount can be obtained from the simulation load data set in the simulation data queue in combination with the current test stress scenario, so as to better simulate the stress test situation of the test.

[0057] S140, the simulation load generation object obtains the target data fusion and dimensionality reduction strategy and the target data receiving object corresponding to the current load generation object type, processes the simulation load data set based on the target data fusion and dimensionality reduction strategy to obtain the simulation processing results and simulation processing performance parameters, and sends them to the target data receiving object.

[0058] In this embodiment, when the simulated load data set is obtained at the simulated load generation object, the simulated load generation object can be a simulated convergence gateway that performs data fusion and dimensionality reduction on the simulated load data set, or it can be a simulated cloud server that performs summary analysis on the simulated load data set. Specifically, the simulated load generation object needs to obtain the target data fusion and dimensionality reduction strategy and target data receiving object corresponding to the current load generation object type. For example, when the simulated load generation object determines that the current load generation object type is a convergence gateway, it is necessary to perform data fusion and dimensionality reduction processing on the simulated load data set corresponding to the simulated convergence gateway, and record the simulation processing process performance parameters during the simulation test. After completing the simulation test, the obtained simulation processing results and simulation processing process performance parameters are sent to the target data receiving object, and at this time the target data receiving object is the convergence gateway.

[0059] In one embodiment, as a first embodiment of performing a simulation test on a simulation load data set based on a simulation load generation object, as shown in FIG. Figure 6As shown, step S140 includes:

[0060] S141A: If it is determined that the current load generating object is a convergence gateway type, a data fusion and dimension reduction strategy including a data compression and dimension reduction sub-strategy is used as the target data fusion and dimension reduction strategy, and the convergence gateway is used as the target data receiving object;

[0061] S142A. Process the simulation load data set accordingly based on the target data fusion and dimensionality reduction strategy to obtain simulation processing results and corresponding simulation processing process performance parameters; wherein the simulation processing process performance parameters at least include the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0062] In this embodiment, as the first embodiment of simulating and testing a simulated load data set based on a simulated load generating object, when it is determined that the current load generating object is a convergence gateway type, the simulated load generating object simulates the convergence gateway to perform data fusion and dimensionality reduction processing on the simulated load data set, and records the simulation processing performance parameters during the simulation test.

[0063] In the specific implementation, the data fusion dimension reduction strategy including the data compression dimension reduction sub-strategy is used as the target data fusion dimension reduction strategy to process the simulated load data set accordingly to obtain the simulation processing result. For example, each simulated load data in the simulated load data set represents the health status data of the sensor acquisition node, and it is further determined that each health status data does not exceed the preset node survival threshold, which means that each health status data can be processed by the simulated load generation object based on the target data fusion dimension reduction strategy.

[0064] Afterwards, taking the dimensionality reduction and compression of a simulated load data in the simulated load data set as an example, the target compressed sequence data corresponding to the simulated load data is determined in multiple local compressed sequence data based on the target data fusion dimensionality reduction strategy; wherein, the target compressed sequence data has at least one free storage bit; if it is determined that the health status data corresponding to the simulated load data does not exceed the node survival threshold and corresponds to normal state data, then based on the target data fusion dimensionality reduction strategy, the simulated load data is converted into a first preset state identification value (for example, the first preset state identification value is 1), and stored in the target compressed sequence data, and the target compressed sequence data is updated; if it is determined that the health status data corresponding to the simulated load data does not exceed the node survival threshold and corresponds to abnormal state data, then based on the target data fusion dimensionality reduction strategy, the simulated load data is converted into a second preset state identification value (for example, the second preset state identification value is 0), and stored in the target compressed sequence data, and the target compressed sequence data is updated. It can be seen that through the above method, the simulated load generation object performs dimensionality reduction compression on each simulated load data, and converts the simulated load data containing multi-dimensional information and data into a one-dimensional first preset state identification value or a second preset state identification value for storage, thereby effectively simulating the processing process of the aggregation gateway.

[0065] Moreover, during the above-mentioned simulation test process, the simulation load generation object will completely record the simulation processing process performance parameters during the simulation test process, which at least includes the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0066] In one embodiment, as a second embodiment of performing a simulation test on a simulation load data set based on a simulation load generation object, as shown in FIG. Figure 7 As shown, step S140 includes:

[0067] S141B, if it is determined that the current load generating object is a cloud server type, the data fusion dimension reduction strategy including the data fusion sub-strategy is used as the target data fusion dimension reduction strategy, and the cloud server is used as the target data receiving object;

[0068] S142B. Process the simulation load data set accordingly based on the target data fusion dimension reduction strategy to obtain simulation processing results and corresponding simulation processing process performance parameters; wherein the simulation processing process performance parameters at least include the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0069] In this embodiment, as the second embodiment of simulating and testing a simulated load data set based on a simulated load generating object, when it is determined that the current load generating object is a cloud server type, the simulated load generating object simulates the cloud server to perform data fusion and dimensionality reduction processing on the simulated load data set, and records the simulation processing performance parameters during the simulation test.

[0070] In the specific implementation, the data fusion dimensionality reduction strategy including the data fusion sub-strategy is used as the target data fusion dimensionality reduction strategy to perform corresponding processing on the simulated load data set to obtain the simulation processing result. For example, the simulated load data set corresponding to the simulated load data of the same aggregation gateway is firstly aggregated and fused separately (the above process realizes the grouped aggregation and fusion of the simulated load data set), and then the simulated load data of each group is processed accordingly in the simulated load generation object to obtain the simulation processing result. It can be seen that through the above method, the convergence and fusion of the simulated load generation object for the simulated load data set is realized, and the processing process of the cloud server is effectively simulated.

[0071] Moreover, during the above-mentioned simulation test process, the simulation load generation object will completely record the simulation processing process performance parameters during the simulation test process, which at least includes the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0072] In one embodiment, after step S140, the method further includes:

[0073] If the simulation load generating object detects a simulation end instruction, the working state of the simulation load generating object is switched to a standby state.

[0074] In this embodiment, after the simulated load generating object completes the current simulation test, it does not need to continue to maintain its active working state, but can switch its working state to a standby state to wait for the next simulation test and switch to an active working state again.

[0075] It can be seen that the implementation example of this method can integrate the simulation fusion computing gateway into the sensor acquisition sub-network, and the simulation fusion computing gateway can directly perform simulation stress testing in various scenarios in the sensor operating environment, thereby improving the effectiveness and efficiency of the simulation stress testing.

[0076] Figure 8 1 is a schematic block diagram of a simulation device for data fusion and dimensionality reduction between sensor network devices provided by an embodiment of the present invention. Figure 8 As shown, corresponding to the above simulation method for data fusion and dimensionality reduction between sensor network devices, the present invention also provides a simulation device 100 for data fusion and dimensionality reduction between sensor network devices. Figure 1 and Figure 8 As shown, the simulation device 100 for data fusion and dimensionality reduction between sensor network devices is configured in a simulation fusion computing gateway 10 (which can be understood as an edge server, edge gateway, etc.), and the simulation fusion computing gateway 10 is communicatively connected with a cloud server 20 and a convergence gateway 30 (which can also be understood as an edge server, edge gateway, etc.), and the convergence gateway is communicatively connected with a sensor acquisition subnet 40. Of course, the convergence gateway 30 can also be communicatively connected with the cloud server 20. The simulation device 100 for data fusion and dimensionality reduction between sensor network devices includes: a simulation data generation unit 110, a simulation data queue unit 120, a simulation load loading unit 130, and a simulation load generation unit 140.

[0077] The simulation data generation unit 110 is used to obtain the current load generation object type in response to the simulation instruction, and generate a return simulation data set based on the current load generation object type and a preset simulation generation strategy.

[0078] The current load generating object type is one of the load generating object types in the load generating object type set, and the load generating object type set includes at least a cloud server type and an aggregation gateway type.

[0079] In this embodiment, the technical solution is described with the simulation fusion computing gateway as the execution subject. Figure 1 As shown, the simulation fusion computing gateway 10 can be in the same sensor acquisition subnetwork (and the complete sensor acquisition network includes multiple sensor acquisition subnetworks) with the convergence gateway 30 and the sensor acquisition subnetwork 40 (the sensor acquisition subnetwork 40 includes multiple sensor acquisition nodes, each of which is deployed on a user terminal device, such as a desktop computer, a laptop computer, a local server, etc.). Through the above system architecture, the simulation fusion computing gateway is integrated into the sensor acquisition subnetwork, and the simulation fusion computing gateway can directly perform simulation stress testing in the sensor operating environment.

[0080] Specifically, the simulation fusion computing gateway first performs real-time detection on the simulation instruction. When the simulation instruction generated by the test start virtual button on the user interaction interface of the manual operation simulation fusion computing gateway is detected, or when the simulation instruction generated based on the preset timing test strategy in the detection simulation fusion computing gateway is detected, it is necessary to further obtain the current load generation object type. Because the simulation fusion computing gateway can simulate the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet, and can also simulate the data generated by the convergence gateway after the sensor node data generated by multiple sensor acquisition nodes are aggregated and reduced in dimension, the current load generation object of the simulation fusion computing gateway is specifically one of the load generation object types in the load generation object type set, and the load generation object type set includes at least a cloud server type and an aggregation gateway type. For example, when the simulation fusion computing gateway simulates the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet, the corresponding current load generation object type is the aggregation gateway type; when the simulation fusion computing gateway simulates the data generated by the convergence gateway after the sensor node data generated by multiple sensor acquisition nodes are aggregated and reduced in dimension, the corresponding current load generation object type is the cloud server type.

[0081] After determining the current load generation object type of this simulation test, the simulation data generator (which can also be regarded as a simulation data generation unit) in the simulation fusion computing gateway can be combined with the simulation generation strategy to generate a feedback simulation data set for subsequent simulation tests.

[0082] In one embodiment, as a first embodiment of generating a return simulation data set, the simulation data generating unit 110 is specifically used to:

[0083] If it is determined that the current load generation object type is a convergence gateway type, obtaining service logic information corresponding to the sensor acquisition subnet from the convergence gateway;

[0084] Based on the simulation generation strategy and the multiple business logic types and business logic sequences included in the business logic information, the return simulation data set corresponding to the business logic information is generated.

[0085] In this embodiment, if it is determined that the current load generation object type is a convergence gateway type, it means that the simulation fusion computing gateway is simulating the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet. As a first embodiment of generating a return simulation data set, it can be to generate multiple return simulation data in a manner that follows business logic information to generate close to real data, and form the return simulation data set.

[0086] Specifically, first parse and obtain the multiple business logic types and business logic sequences included in the business logic information; then obtain multiple initial simulation data; then process the multiple initial simulation data based on the multiple business logic types, business logic sequences and specific calculation methods corresponding to each business logic in the business logic information to obtain multiple return simulation data; finally, the return simulation data set is composed of the multiple return simulation data obtained above. It can be seen that based on the above method, a large amount of return simulation data for simulation testing can be quickly generated, thereby forming a return simulation data set.

[0087] In one embodiment, as a second embodiment of generating a return simulation data set, the simulation data generating unit 110 is specifically used to:

[0088] If it is determined that the current load generation object type is a convergence gateway type or a cloud server type, a historical business data set corresponding to the sensor acquisition subnet is obtained from the convergence gateway; wherein the historical business data set includes multiple business scenario data subsets, each business scenario data subset corresponds to a unique business scenario in the sensor acquisition subnet;

[0089] The multiple business scenario data subsets included in the historical business data set are amplified and replayed based on the simulation generation strategy to obtain processed data subsets corresponding to each business scenario data subset, and form the return simulation data set.

[0090] In this embodiment, if it is determined that the current load generation object type is a convergence gateway type, it means that the simulation fusion computing gateway is simulating the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet; if it is determined that the current load generation object type is a cloud server type, it means that the simulation fusion computing gateway is simulating the convergence gateway to aggregate and reduce the sensor node data generated by multiple sensor acquisition nodes. As a second embodiment of generating a return simulation data set, it can be to generate multiple return simulation data by amplifying and replaying historical data, and form the return simulation data set.

[0091] If the simulation fusion computing gateway is used to simulate the sensor node data generated by multiple sensor acquisition nodes in the sensor acquisition subnet, the specific method is to first obtain the historical business data set corresponding to the sensor acquisition subnet from the convergence gateway; wherein the historical business data set includes multiple business scenario data subsets, and each business scenario data subset corresponds to a unique business scenario in the sensor acquisition subnet. For example, the business scenarios included in the sensor acquisition subnet at least include the addition of sensor nodes, sensor node offline, sensor failure abnormal offline, sensor data volume surge, sensor acquisition data large-scale abnormality, and sensor subnet increase. Moreover, the historical business data set in this application takes into account the business scenario data subsets for multiple business scenarios in the sensor acquisition subnet. The purpose is to address the problems that the entire sensor acquisition network may encounter in real operation under different business scenarios in the real production environment, so as to adjust the operating parameters of the equipment deployed by the key category sensor acquisition nodes and achieve structural operation optimization.

[0092] Then, historical business data of a preset data volume are selected from all business scenario data subsets or part of the business scenario data subsets in the multiple business scenario data subsets included in the historical business data set. For example, the historical business data set includes N1 business scenario data subsets (N1 is a positive integer), and they are respectively recorded as the 1st business scenario data subset to the N1th business scenario data subset, then the 1st business scenario data subset (for example, corresponding to the business scenario of adding a sensor node) and the 2nd business scenario data subset (for example, corresponding to the business scenario of the sensor node going offline) are first selected, and then N2 historical business data are selected from the 1st business scenario data subset (N2 represents the preset data volume, N2 is a positive integer, and N2 is less than the 1st business scenario data subset). The total number of historical business data in the first business scenario data subset), and N2 historical business data are selected from the second business scenario data subset (N2 is less than the total number of historical business data in the second business scenario data subset), and finally N2 historical business data are selected from the first business scenario data subset and N2 historical business data are selected from the second business scenario data subset to amplify and replay the historical data based on the simulation generation strategy (data amplification can be understood as expanding the data scale of small-scale data based on a specified data amplification strategy), thereby obtaining the first processed data subset corresponding to the N2 historical business data in the first business scenario data subset, and the second processed data subset corresponding to the N2 historical business data in the second business scenario data subset. Finally, the multiple processed data subsets obtained above constitute the return simulation data set. It can be seen that based on the above method, a large number of processed data subsets for simulation testing can be quickly generated to constitute a return simulation data set.

[0093] The simulation data queue unit 120 is used to send the returned simulation data set to a simulation data queue.

[0094] In this embodiment, when the simulation fusion computing gateway completes the acquisition of the returned simulation data set, it can be first stored in the simulation data queue, which is a load data queue, so that subsequent simulation tests can load and obtain the returned simulation data set at high speed from the simulation data queue.

[0095] The simulation load loading unit 130 is used to obtain a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and send the simulation load data set to a simulation load generation object.

[0096] In this embodiment, when the simulation test is specifically started, the simulation data load loader in the simulation fusion computing gateway may first call the simulation load loading strategy to obtain the simulation load data set from the simulation data queue, and then send the simulation load data set to the simulation load generation object. Among them, the simulation load generation object corresponds to the current load generation object type, that is, when the current load generation object type is a convergence gateway type, the simulation load generation object is a convergence gateway object; when the current load generation object type is a cloud server type, the simulation load generation object is a cloud server object. It can be seen that based on the above method, the simulation load data set can be obtained from the simulation data queue based on the simulation load generation object in the simulation fusion computing gateway.

[0097] In one embodiment, the simulated load loading unit 130 is specifically used for:

[0098] If it is determined that the current load occurrence object type is a converged gateway type or a cloud server type, the current test stress scenario corresponding to the converged gateway type or the cloud server type is obtained based on the simulation load loading strategy, and the current data acquisition amount corresponding to the current test stress scenario is determined;

[0099] Based on the current data acquisition amount, a corresponding amount of simulation load data is acquired from the simulation data queue to form and update the simulation load data set, and the simulation load data set is sent to the simulation load generation object.

[0100] In this embodiment, if it is determined that the current load generation object type is a convergence gateway type or a cloud server type, it means that when the current load generation object type is a convergence gateway type, the simulated load generation object is used to simulate multiple sensor acquisition nodes in the sensor acquisition subnet to perform a simulation test on the convergence gateway, or when the current load generation object type is a cloud server type, the simulated load generation object is used to simulate multiple convergence gateways to perform a simulation test on the cloud server. However, no matter whether the final simulation test object of the simulated load generation object is a convergence gateway or a cloud server, the current test pressure scenario can be determined first (such as close to the real production scenario, completely simulated real production scenario, extreme pressure scenario, etc.), and then the current data acquisition amount is determined according to the current test pressure scenario. Finally, after determining the current data acquisition amount, the simulated load data corresponding to the current data acquisition amount is obtained from the simulated load data set in the simulated data queue to form and update the simulated load data set, and the simulated load data set is sent to the simulated load generation object. It can be seen that based on the above method, the simulation load data corresponding to the current data acquisition amount can be obtained from the simulation load data set in the simulation data queue in combination with the current test stress scenario, so as to better simulate the stress test situation of the test.

[0101] The simulation load generation unit 140 is used for the simulation load generation object to obtain the target data fusion and dimensionality reduction strategy and the target data receiving object corresponding to the current load generation object type, and to process the simulation load data set accordingly based on the target data fusion and dimensionality reduction strategy to obtain the simulation processing results and simulation processing performance parameters, and send them to the target data receiving object.

[0102] In this embodiment, when the simulated load data set is obtained at the simulated load generation object, the simulated load generation object can be a simulated convergence gateway that performs data fusion and dimensionality reduction on the simulated load data set, or it can be a simulated cloud server that performs summary analysis on the simulated load data set. Specifically, the simulated load generation object needs to obtain the target data fusion and dimensionality reduction strategy and target data receiving object corresponding to the current load generation object type. For example, when the simulated load generation object determines that the current load generation object type is a convergence gateway, it is necessary to perform data fusion and dimensionality reduction processing on the simulated load data set corresponding to the simulated convergence gateway, and record the simulation processing process performance parameters during the simulation test. After completing the simulation test, the obtained simulation processing results and simulation processing process performance parameters are sent to the target data receiving object, and at this time the target data receiving object is the convergence gateway.

[0103] In one embodiment, as a first embodiment of performing a simulation test on a simulation load data set based on a simulation load generation object, the simulation load generation unit 140 is specifically used to:

[0104] If it is determined that the current load generating object is of the aggregation gateway type, the data fusion and dimension reduction strategy including the data compression and dimension reduction sub-strategy is used as the target data fusion and dimension reduction strategy, and the aggregation gateway is used as the target data receiving object;

[0105] The simulation load data set is processed accordingly based on the target data fusion dimension reduction strategy to obtain simulation processing results and corresponding simulation processing process performance parameters; wherein the simulation processing process performance parameters at least include the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0106] In this embodiment, as the first embodiment of simulating and testing a simulated load data set based on a simulated load generating object, when it is determined that the current load generating object is a convergence gateway type, the simulated load generating object simulates the convergence gateway to perform data fusion and dimensionality reduction processing on the simulated load data set, and records the simulation processing performance parameters during the simulation test.

[0107] In the specific implementation, the data fusion dimension reduction strategy including the data compression dimension reduction sub-strategy is used as the target data fusion dimension reduction strategy to process the simulated load data set accordingly to obtain the simulation processing result. For example, each simulated load data in the simulated load data set represents the health status data of the sensor acquisition node, and it is further determined that each health status data does not exceed the preset node survival threshold, which means that each health status data is data that can be processed by the simulated load generation object based on the target data fusion dimension reduction strategy.

[0108] Afterwards, taking the dimensionality reduction and compression of a simulated load data in the simulated load data set as an example, the target compressed sequence data corresponding to the simulated load data is determined in multiple local compressed sequence data based on the target data fusion dimensionality reduction strategy; wherein, the target compressed sequence data has at least one free storage bit; if it is determined that the health status data corresponding to the simulated load data does not exceed the node survival threshold and corresponds to normal state data, then based on the target data fusion dimensionality reduction strategy, the simulated load data is converted into a first preset state identification value (for example, the first preset state identification value is 1), and stored in the target compressed sequence data, and the target compressed sequence data is updated; if it is determined that the health status data corresponding to the simulated load data does not exceed the node survival threshold and corresponds to abnormal state data, then based on the target data fusion dimensionality reduction strategy, the simulated load data is converted into a second preset state identification value (for example, the second preset state identification value is 0), and stored in the target compressed sequence data, and the target compressed sequence data is updated. It can be seen that through the above method, the simulated load generation object performs dimensionality reduction compression on each simulated load data, and converts the simulated load data containing multi-dimensional information and data into a one-dimensional first preset state identification value or a second preset state identification value for storage, thereby effectively simulating the processing process of the aggregation gateway.

[0109] Moreover, during the above-mentioned simulation test process, the simulation load generation object will completely record the simulation processing process performance parameters during the simulation test process, which at least includes the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0110] In one embodiment, as a second embodiment of performing a simulation test on a simulation load data set based on a simulation load generation object, the simulation load generation unit 140 is specifically used to:

[0111] If it is determined that the current load generating object is a cloud server type, the data fusion dimension reduction strategy including the data fusion sub-strategy is used as the target data fusion dimension reduction strategy, and the cloud server is used as the target data receiving object;

[0112] The simulation load data set is processed accordingly based on the target data fusion dimension reduction strategy to obtain simulation processing results and corresponding simulation processing process performance parameters; wherein the simulation processing process performance parameters at least include the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0113] In this embodiment, as the second embodiment of simulating and testing a simulated load data set based on a simulated load generating object, when it is determined that the current load generating object is a cloud server type, the simulated load generating object simulates the cloud server to perform data fusion and dimensionality reduction processing on the simulated load data set, and records the simulation processing performance parameters during the simulation test.

[0114] In the specific implementation, the data fusion dimensionality reduction strategy including the data fusion sub-strategy is used as the target data fusion dimensionality reduction strategy to perform corresponding processing on the simulated load data set to obtain the simulation processing result. For example, the simulated load data set corresponding to the simulated load data of the same aggregation gateway is firstly aggregated and fused separately (the above process realizes the grouped aggregation and fusion of the simulated load data set), and then the simulated load data of each group is processed accordingly in the simulated load generation object to obtain the simulation processing result. It can be seen that through the above method, the convergence and fusion of the simulated load generation object for the simulated load data set is realized, and the processing process of the cloud server is effectively simulated.

[0115] Moreover, during the above-mentioned simulation test process, the simulation load generation object will completely record the simulation processing process performance parameters during the simulation test process, which at least includes the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

[0116] In one embodiment, the simulated load generating unit 140 is further used for:

[0117] If the simulation load generating object detects a simulation end instruction, the working state of the simulation load generating object is switched to a standby state.

[0118] In this embodiment, after the simulated load generating object completes the current simulation test, it does not need to continue to maintain its active working state, but can switch its working state to a standby state to wait for the next simulation test and switch to an active working state again.

[0119] It can be seen that the implementation example of the device integrates the simulation fusion computing gateway into the sensor acquisition sub-network, and the simulation fusion computing gateway can directly perform simulation stress testing in various scenarios in the sensor operating environment, thereby improving the effectiveness and efficiency of the simulation stress testing.

[0120] The above-mentioned simulation device for data fusion and dimension reduction between sensor network devices can be implemented in the form of a computer program. The computer program can be used in Fig. 9 Runs on the computer device shown.

[0121] See also Fig. 9 , Fig. 9The present invention is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device integrates any one of the simulation devices for data fusion and dimension reduction between sensor network devices provided by an embodiment of the present invention.

[0122] See also Fig. 9 The computer device 400 includes a processor 402 , a memory and a network interface 405 connected via a system bus 401 , wherein the memory may include a storage medium 403 and an internal memory 404 .

[0123] The storage medium 403 can store an operating system 4031 and a computer program 4032. The computer program 4032 includes program instructions, and when the program instructions are executed, the processor 402 can execute the above-mentioned simulation method for data fusion and dimensionality reduction between sensor network devices.

[0124] The processor 402 is used to provide computing and control capabilities to support the operation of the entire computer device.

[0125] The internal memory 404 provides an environment for the operation of the computer program 4032 in the storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute the above-mentioned simulation method for data fusion and dimensionality reduction between sensor network devices.

[0126] The network interface 405 is used to communicate with other devices over the network. Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] The processor 402 is used to run the computer program 4032 stored in the memory to implement the simulation method of data fusion and dimensionality reduction between sensor network devices as described above.

[0128] It should be understood that in the embodiment of the present invention, the processor 402 may be a central processing unit (CPU), and the processor 402 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0129] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer device to implement the process steps of the embodiment of the above method.

[0130] Therefore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the simulation method for data fusion and dimensionality reduction between sensor network devices as described above.

[0131] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0132] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0133] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0134] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0136] 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 simulation method for data fusion and dimension reduction between sensor network devices, applied to a simulation fusion computing gateway, characterized in that: The simulation fusion computing gateway is in communication connection with the cloud server and the convergence gateway, and the convergence gateway is in communication connection with the sensor acquisition subnet; the simulation method for data fusion and dimensionality reduction between sensor network devices includes: In response to the simulation instruction, the current load generation object type is obtained, and a return simulation data set is generated based on the current load generation object type and a preset simulation generation strategy; wherein the current load generation object type is one of the load generation object types in the load generation object type set, and the load generation object type set includes at least a cloud server type and an aggregation gateway type; Sending the returned simulation data set to a simulation data queue; Acquire a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and send the simulation load data set to a simulation load generation object; The simulation load generation object obtains the target data fusion dimensionality reduction strategy and the target data receiving object corresponding to the current load generation object type, processes the simulation load data set based on the target data fusion dimensionality reduction strategy to obtain the simulation processing results and simulation processing performance parameters, and sends them to the target data receiving object.

2. The method according to claim 1, characterized in that The generating of the return simulation data set based on the current load generation object type and a preset simulation generation strategy includes: If it is determined that the current load generation object type is a convergence gateway type, obtaining service logic information corresponding to the sensor acquisition subnet from the convergence gateway; Based on the simulation generation strategy and the multiple business logic types and business logic sequences included in the business logic information, the return simulation data set corresponding to the business logic information is generated.

3. The method according to claim 1, characterized in that The generating of the return simulation data set based on the preset simulation generation strategy includes: If it is determined that the current load generation object type is a convergence gateway type or a cloud server type, a historical business data set corresponding to the sensor acquisition subnet is obtained from the convergence gateway; wherein the historical business data set includes multiple business scenario data subsets, each business scenario data subset corresponds to a unique business scenario in the sensor acquisition subnet; The multiple business scenario data subsets included in the historical business data set are amplified and replayed based on the simulation generation strategy to obtain processed data subsets corresponding to each business scenario data subset, and form the return simulation data set.

4. The method according to claim 1, characterized in that: The step of acquiring a simulation load data set from the simulation data queue based on a preset simulation load loading strategy and sending the simulation load data set to a simulation load generation object includes: If it is determined that the current load occurrence object type is a converged gateway type or a cloud server type, the current test stress scenario corresponding to the converged gateway type or the cloud server type is obtained based on the simulation load loading strategy, and the current data acquisition amount corresponding to the current test stress scenario is determined; Based on the current data acquisition amount, a corresponding amount of simulation load data is acquired from the simulation data queue to form and update the simulation load data set, and the simulation load data set is sent to the simulation load generation object.

5. The method according to claim 1, characterized in that The simulated load generation object obtains a target data fusion dimension reduction strategy and a target data receiving object corresponding to the type of the current load generation object, and processes the simulated load data set accordingly based on the target data fusion dimension reduction strategy to obtain a simulation processing result and a simulation processing process performance parameter, including: If it is determined that the current load generating object is of the aggregation gateway type, the data fusion and dimension reduction strategy including the data compression and dimension reduction sub-strategy is used as the target data fusion and dimension reduction strategy, and the aggregation gateway is used as the target data receiving object; The simulation load data set is processed accordingly based on the target data fusion dimension reduction strategy to obtain simulation processing results and corresponding simulation processing process performance parameters; wherein the simulation processing process performance parameters at least include the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

6. The method according to claim 1, characterized in that The simulated load generation object obtains a target data fusion dimension reduction strategy and a target data receiving object corresponding to the type of the current load generation object, and processes the simulated load data set accordingly based on the target data fusion dimension reduction strategy to obtain a simulation processing result and a simulation processing process performance parameter, including: If it is determined that the current load generating object is a cloud server type, the data fusion dimension reduction strategy including the data fusion sub-strategy is used as the target data fusion dimension reduction strategy, and the cloud server is used as the target data receiving object; The simulation load data set is processed accordingly based on the target data fusion dimension reduction strategy to obtain simulation processing results and corresponding simulation processing process performance parameters; wherein the simulation processing process performance parameters at least include the duration of the simulation processing process and the system resource occupancy rate of the simulation processing process.

7. The method according to claim 1, characterized in that After the simulated load generation object obtains the target data fusion dimensionality reduction strategy and the target data receiving object corresponding to the type of the current load generation object, the simulated load data set is processed accordingly based on the target data fusion dimensionality reduction strategy to obtain the simulation processing result and the simulation processing process performance parameter, and sent to the target data receiving object, the method further includes: If the simulation load generating object detects a simulation end instruction, the working state of the simulation load generating object is switched to a standby state.

8. A simulation device for data fusion and dimension reduction between sensor network devices, configured in a simulation fusion computing gateway, characterized in that: The simulation fusion computing gateway is connected to the cloud server and the convergence gateway, and the convergence gateway is connected to the sensor acquisition subnet; the simulation device for data fusion and dimensionality reduction between sensor network devices includes: A simulation data generation unit, configured to obtain the current load generation object type in response to a simulation instruction, and generate a return simulation data set based on the current load generation object type and a preset simulation generation strategy; wherein the current load generation object type is one of the load generation object types in the load generation object type set, and the load generation object type set includes at least a cloud server type and an aggregation gateway type; A simulation data queue unit, sending the returned simulation data set to a simulation data queue; A simulation load loading unit, configured to obtain a simulation load data set from the simulation data queue based on a preset simulation load loading strategy, and send the simulation load data set to a simulation load generation object; The simulation load generation unit is used for the simulation load generation object to obtain the target data fusion and dimensionality reduction strategy and the target data receiving object corresponding to the current load generation object type, and to process the simulation load data set accordingly based on the target data fusion and dimensionality reduction strategy to obtain the simulation processing results and simulation processing performance parameters, and send them to the target data receiving object.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the simulation method for data fusion and dimensionality reduction between sensor network devices as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the simulation method for data fusion and dimensionality reduction between sensor network devices according to any one of claims 1 to 7 can be implemented.

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