A self-adaptive protocol conversion and cooperative control method for smart park equipment

Abstract

The application relates to the technical field of smart parks, in particular to a self-adaptive protocol conversion and collaborative control method for a smart park device; the method comprises the following steps: generating an initial attribute set based on a device communication protocol and a running frequency, determining a protocol conversion rule through clustering analysis, dynamically adapting a new protocol and adjusting a frequency deviation, generating a collaborative task time sequence arrangement, optimizing control response and data interaction flow when a sudden event or transmission delay occurs, and finally realizing a stable running state through a feedback mechanism. The application can efficiently extract protocol features, flexibly adapt new device protocols, solve the collaborative problems caused by frequency differences, improve the synchronization accuracy between devices and the overall collaborative efficiency, and ensure the stability and reliability of the system in complex scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart park, and in particular to a self-adaptive protocol conversion and collaborative control method for smart park equipment. BACKGROUND

[0002] As a core component of modern city management, smart park improves operational efficiency by integrating environmental monitoring sensors, security cameras, intelligent lighting and other devices, but device interconnection, interoperability and collaborative control are still the core bottlenecks restricting the level of intelligence.

[0003] There are three key problems in the prior art: first, protocol conversion relies on static adapters, which only adapt to specific devices. When new devices (such as new sensors) are added, there is no corresponding conversion rule, which can easily cause data interaction delay or failure; second, collaborative control is based on fixed timing scheduling. In the face of unexpected events (such as security anomalies), it is not possible to dynamically adjust the action logic, resulting in a lag in response and insufficient consistency; third, the running frequency of devices varies significantly (such as security cameras that need to work in real time and lighting that needs to be dynamically dimmed). The existing mechanism lacks dynamic frequency coordination capability, which can easily cause action timing misalignment (such as security and lighting linkage out of sync), resulting in resource waste or functional blind spots.

[0004] For example, when security detection is abnormal at night, the frequency mismatch will cause the lighting to respond with a lag; if the protocol of the newly connected intelligent parking lock is not adapted, the parking status cannot be transmitted to the navigation system in real time, directly affecting the user experience. How to achieve seamless interaction and collaborative control of devices in an environment with dynamic protocol changes and different frequencies is a key challenge for smart park device management. SUMMARY

[0005] The purpose of the present application is to provide a self-adaptive protocol conversion and collaborative control method for smart park equipment to overcome the above-mentioned deficiencies in the prior art.

[0006] The purpose of the present application is achieved by the following technical solution: a self-adaptive protocol conversion and collaborative control method for smart park equipment, comprising the following steps:

[0007] S1, based on the communication protocol type and running frequency data of all devices in the park, extracting protocol format features and frequency period parameters, and generating an initial device attribute set;

[0008] S2, based on the initial device attribute set, using a clustering analysis method to group process the protocol types, and determining the conversion rule mapping relationship between the groups of protocols;

[0009] S3, if there is a new device protocol that is not covered in the conversion rule mapping relationship, then through the protocol analysis module, the data packets of the new device are decoded and feature matched in real time to obtain dynamically adapted conversion parameters;

[0010] S4, extract the deviation value of the frequency period parameter from the dynamically adapted conversion parameter, judge whether the deviation value exceeds the preset threshold value, if yes, use the frequency adjustment algorithm to synchronize and calibrate the running period of the related device, and generate the coordinated frequency sequence;

[0011] S5, based on the coordinated frequency sequence, obtain the cooperative task demand between the associated devices, apply the sequence to the device control instruction through the task allocation module, and generate the accurate action time sequence arrangement;

[0012] S6, if a sudden event signal is detected in the action time sequence arrangement, extract the priority information from the event response attribute, adjust the frequency parameter in the time sequence arrangement by using the dynamic scheduling method, and generate the optimized control response sequence;

[0013] S7, based on the optimized control response sequence, verify the data transmission of the interconnection channel of the park device, judge whether the transmission delay is within the acceptable range, if not, re-allocate the transmission path through the channel optimization module, and generate the stable data interaction flow;

[0014] S8, monitor the overall cooperative efficiency index from the stable data interaction flow, and use the feedback loop mechanism to iteratively update the frequency sequence and the conversion parameter, and generate the final running stable state.

[0015] The application further provides that the initial device attribute set includes protocol format features, frequency period parameters and device function classification, the protocol conversion rule mapping relationship includes protocol field correspondence, data packet structure mapping and communication logic adaptation strategy, the dynamically adapted conversion parameter includes protocol compatibility evaluation, data packet decoding rule and real-time feature matching result, the coordinated frequency sequence includes device running period synchronization point, frequency deviation correction value and cooperative task time window, the accurate action time sequence arrangement includes task execution sequence, device action gap and cooperative response time, the optimized control response sequence includes priority adjustment strategy, frequency parameter dynamic correction and burst task processing logic, the stable data interaction flow includes transmission path distribution, delay optimization record and data integrity verification, and the final running stable state includes cooperative efficiency evaluation index, frequency sequence stability analysis and protocol adaptation optimization suggestion.

[0016] The application further provides that based on the communication protocol type and the running frequency data of all devices in the park, the protocol format features and the frequency period parameters are extracted, and the step of generating the initial device attribute set is specifically:

[0017] Based on the communication protocol type and running frequency data of the equipment in the park, a multi-dimensional data analysis method is used to identify the protocol field structure and frequency cycle characteristics, extract the protocol format features and frequency cycle parameters, and generate a preliminary equipment attribute set;

[0018] Based on the preliminary equipment attribute set, the semantic information of the protocol field and the fluctuation law of the frequency cycle are deeply mined using pattern recognition technology, and detailed descriptions of the protocol format features and frequency cycle parameters are generated.

[0019] Based on the detailed descriptions of the protocol format features and frequency cycle parameters, a hierarchical classification method is used to organize the attribute set according to the function category and running characteristics of the equipment, and an initial equipment attribute set is generated.

[0020] The application further provides that, based on the initial equipment attribute set, a clustering analysis method is used to group the protocols and determine the conversion rule mapping relationship between the groups.

[0021] Based on the initial equipment attribute set, a clustering analysis method is used to group the protocols according to the similarity of the protocol format features and the correlation of the frequency cycle parameters, and a protocol grouping result is generated.

[0022] Based on the protocol grouping result, rule derivation technology is used to analyze the field mapping relationship and data packet structure conversion logic between the groups, and a protocol conversion rule mapping relationship is generated.

[0023] Based on the protocol conversion rule mapping relationship, a consistency verification method is used to test and optimize the accuracy and applicability of the mapping relationship, and ensure the effectiveness of the protocol conversion.

[0024] The application further provides that, if there is a new device protocol that is not covered in the conversion rule mapping relationship, a protocol analysis module is used to decode and feature match the data packet of the new device in real time, and obtain dynamically adapted conversion parameters.

[0025] If there is a new device protocol that is not covered in the conversion rule mapping relationship, start the protocol analysis module, decode the data packet of the new device in real time, and extract the key fields and communication features of the data packet.

[0026] Based on the key fields and communication features, a feature matching technology is used to compare the new device protocol with the existing protocols, and dynamically adapted conversion parameters are generated.

[0027] Based on the dynamically adapted conversion parameters, a protocol compatibility evaluation method is used to verify and optimize the adaptation effect of the new device protocol, and ensure the seamless connection of the protocol conversion.

[0028] The application further provides that the deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameter, it is judged whether the deviation value exceeds a preset threshold, if it exceeds, a frequency adjustment algorithm is used to synchronize and calibrate the operation cycle of the related device, and the steps of generating the coordinated frequency sequence are specifically as follows:

[0029] The deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameter, the actual value and the theoretical value of the device operation cycle are compared, and the size of the deviation value is calculated;

[0030] It is judged whether the deviation value exceeds a preset threshold, if it exceeds, a frequency adjustment algorithm is used to synchronize and calibrate the operation cycle of the related device, and the steps of generating the preliminary frequency sequence are specifically as follows:

[0031] Based on the preliminary frequency sequence, a frequency stability analysis method is used to evaluate and optimize the fluctuation of the frequency sequence, and the coordinated frequency sequence is generated.

[0032] The application further provides that based on the coordinated frequency sequence, the cooperative task demand between the related devices is obtained, the sequence is applied to the device control instruction through a task allocation module, and the steps of generating the accurate action time sequence arrangement are specifically as follows:

[0033] Based on the coordinated frequency sequence, the cooperative task demand between the related devices is obtained, and the cooperative task is decomposed into multiple subtasks through a task decomposition technology;

[0034] Based on the subtasks, a task allocation module is used to match the frequency sequence and the device control instruction, and the preliminary action time sequence arrangement is generated;

[0035] Based on the preliminary action time sequence arrangement, a time sequence optimization technology is used to finely adjust the action gap and the response time, and the accurate action time sequence arrangement is generated.

[0036] The application further provides that if a sudden event signal is detected in the action time sequence arrangement, priority information is extracted from the event response attribute, a dynamic scheduling method is used to adjust the frequency parameter in the time sequence arrangement, and the steps of generating the optimized control response sequence are specifically as follows:

[0037] If a sudden event signal is detected in the action time sequence arrangement, an event response module is started, and priority information is extracted from the event response attribute;

[0038] Based on the priority information, a dynamic scheduling method is used to real-time adjust the frequency parameter in the time sequence arrangement, and the preliminary control response sequence is generated;

[0039] Based on the preliminary control response sequence, a priority optimization technique is used to optimize the task execution logic of the control response sequence, and an optimized control response sequence is generated.

[0040] The application further provides that, based on the optimized control response sequence, the interconnection channel of the park equipment is verified for data transmission, and it is judged whether the transmission delay is within an acceptable range, and if not, the transmission path is re-distributed through the channel optimization module to generate a stable data interaction flow.

[0041] Based on the optimized control response sequence, the interconnection channel of the park equipment is verified for data transmission, and it is judged whether the transmission delay is within an acceptable range through delay monitoring technology.

[0042] If the transmission delay exceeds the acceptable range, the channel optimization module is started to re-distribute the transmission path to generate a preliminary data interaction flow.

[0043] Based on the preliminary data interaction flow, a path stability analysis method is used to evaluate the reliability and delay optimization effect of the transmission path to generate a stable data interaction flow.

[0044] The application further provides that the overall coordination efficiency index is monitored from the stable data interaction flow, and a feedback loop mechanism is used to iteratively update the frequency sequence and conversion parameters to generate a final running stable state.

[0045] The overall coordination efficiency index is monitored from the stable data interaction flow, and the trend of the coordination efficiency is analyzed through efficiency evaluation technology.

[0046] Based on the trend of the coordination efficiency, a feedback loop mechanism is used to iteratively update the frequency sequence and conversion parameters to generate a preliminary running stable state.

[0047] Based on the preliminary running stable state, a stability verification method is used to evaluate the persistence and reliability of the running stable state to generate a final running stable state.

[0048] The application has the following advantages: The application realizes efficient management of the communication protocol and running frequency of the smart park equipment through multi-dimensional data analysis, clustering analysis, dynamic adaptation and frequency adjustment, and significantly improves the coordination efficiency and running stability between devices. BRIEF DESCRIPTION OF DRAWINGS

[0049] The application is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the application, and other embodiments can be obtained by those skilled in the art without creative labor under the premise of not paying creative labor.

[0050] Figure 1 is a method flowchart of the present application. DETAILED DESCRIPTION

[0051] The present application is further described in conjunction with the following examples.

[0052] From Figure 1 It can be known that the core of the adaptive protocol conversion and collaborative control method of the smart park equipment described in the embodiment is to realize efficient management of the communication protocols and running frequencies of the equipment in the park through multi-dimensional data analysis, cluster analysis, dynamic adaptation, and frequency adjustment, so as to improve the collaborative efficiency and running stability between the equipment.

[0053] In actual application, assuming that a smart park contains multiple types of equipment, such as lighting systems, security cameras, environmental monitoring sensors, intelligent access control systems, etc. These devices may use different communication protocols (such as Modbus, Zigbee, MQTT, etc.) and work at different running frequencies. In order to realize efficient collaborative management of these devices, it is necessary to first generate an initial set of device attributes. First, based on the communication protocol types and running frequency data of all devices in the park, a multi-dimensional data analysis method is used to identify the protocol field structure and frequency period characteristics, extract protocol format features and frequency period parameters, and generate a preliminary set of device attributes. This process can be completed by a data acquisition module that can read the communication data of the device in real time and extract key information using analysis algorithms, such as extracting register addresses, function codes, etc. for Modbus protocol; for Zigbee protocol, network ID, node address, etc. can be extracted. At the same time, the frequency period characteristics can be determined by counting the time interval of the device sending data. Then, the pattern recognition technology is used to deeply mine the semantic information of the protocol field and the fluctuation law of the frequency period, and generate a detailed description of the protocol format features and frequency period parameters. This process can use classification algorithms in machine learning, such as decision trees or support vector machines, to semantically annotate the protocol fields and predict the trend of the frequency period. Finally, a hierarchical classification method is used to organize the attribute set according to the function categories and running characteristics of the equipment, and generate an initial set of device attributes, such as classifying the lighting system into the "energy management" category and the security camera into the "security monitoring" category, so as to facilitate subsequent processing.

[0054] After generating the initial device attribute set, step S2 is entered, that is, the protocol types are grouped and processed based on the initial device attribute set using a clustering analysis method to determine the conversion rule mapping relationship between groups of protocols. First, based on the initial device attribute set, a clustering analysis method is used to group and process the protocol types according to the similarity of the protocol format features and the correlation of the frequency period parameters, generate a protocol grouping result, and common clustering algorithms include K-means and DBSCAN, wherein K-means is suitable for cases where the protocol format features are relatively regular, and DBSCAN is more suitable for cases where the protocol features are unevenly distributed. For example, assume that there are three main protocol types in the park: Modbus, Zigbee, and MQTT. Through clustering analysis, Modbus and MQTT can be grouped together because they both support master-slave communication mode, and Zigbee is separately grouped because it uses a mesh network topology. Then, based on the protocol grouping result, rule derivation technology is used to analyze the field mapping relationship and data packet structure conversion logic between groups of protocols, generate protocol conversion rule mapping relationship, for example, for the conversion between Modbus and MQTT, the field mapping rules can be defined as follows: Modbus register address is mapped to MQTT topic, function code is mapped to message type, in addition, data packet structure conversion logic needs to be designed to ensure seamless data transfer between different protocols. Finally, based on the protocol conversion rule mapping relationship, a consistency verification method is used to test and optimize the accuracy and applicability of the mapping relationship to ensure the effectiveness of protocol conversion, which can be completed through a simulation test environment, that is, the data packets of different protocols are input into the conversion module, and whether the output meets the expectations is observed.

[0055] In some cases, the park may introduce new devices whose communication protocols are not covered by existing conversion rules. At this time, step S3 is entered, and the data packets of the new devices are decoded and feature matched in real time by the protocol analysis module to obtain dynamically adapted conversion parameters. First, if there is an uncovered new device protocol in the conversion rule mapping relationship, start the protocol analysis module to decode the data packets of the new device in real time, extract the key fields and communication characteristics of the data packets, for example, for the newly introduced LoRa device, the protocol analysis module can identify the preamble, frame header and payload fields in its data packet. Then based on the key fields and communication characteristics, use feature matching technology to compare the new device protocol with the existing protocol, and generate dynamically adapted conversion parameters. This process can use a hash algorithm to calculate the similarity of the protocol characteristics, select the closest existing protocol as a reference, for example, if it is found that the LoRa protocol has a high similarity in data packet structure with the Zigbee protocol, then the preliminary conversion parameters of LoRa can be generated based on the conversion rules of Zigbee. Finally, based on the dynamically adapted conversion parameters, use the protocol compatibility evaluation method to verify and optimize the adaptation effect of the new device protocol, and ensure the seamless connection of protocol conversion. This process can be completed through actual scene testing, for example, connecting the LoRa device to the park network and observing whether its communication with other devices is normal.

[0056] After completing the protocol conversion, step S4 is entered, and the deviation value of the frequency period parameter is extracted from the dynamically adapted conversion parameters to determine whether the deviation value exceeds the preset threshold. If it exceeds, the frequency adjustment algorithm is used to synchronize and calibrate the running period of the related device to generate the coordinated frequency sequence. First, the deviation value of the frequency period parameter is extracted from the dynamically adapted conversion parameters, and the size of the deviation value is calculated by comparing the actual value and the theoretical value of the device running period, for example, assuming that the theoretical running period of a device is 1 second, and the actual running period is 1.2 seconds, then the deviation value is 0.2 seconds. Then determine whether the deviation value exceeds the preset threshold, if it exceeds, start the frequency adjustment algorithm to synchronize and calibrate the running period of the related device to generate the preliminary frequency sequence. Common frequency adjustment algorithms include PID control algorithm and Kalman filter algorithm, among which the PID control algorithm dynamically adjusts the running period of the device through proportional, integral and differential items, and the Kalman filter algorithm optimizes the frequency calibration accuracy by predicting the historical data, for example, for the device with a deviation value of 0.2 seconds, the PID control algorithm can be used to adjust its running period to 1 second. Finally, based on the preliminary frequency sequence, use the frequency stability analysis method to evaluate and optimize the fluctuation of the frequency sequence to generate the coordinated frequency sequence. This process can analyze the frequency spectrum characteristics of the frequency sequence through Fourier transform to ensure the stationarity of the frequency sequence.

[0057] After generating the coordinated frequency sequence, proceed to step S5, based on the coordinated frequency sequence, obtain the cooperative task demand between the associated devices, apply the sequence to the device control instruction through the task allocation module, and generate an accurate action timing arrangement. First, based on the coordinated frequency sequence, obtain the cooperative task demand between the associated devices, decompose the cooperative task into multiple sub-tasks through task decomposition technology, for example, assume that the lighting system and the security camera in the park need to work cooperatively, the cooperative task can be decomposed into two sub-tasks of “lighting system turning on” and “camera starting recording”. Then, based on the sub-tasks, use the task allocation module to match the frequency sequence with the device control instruction to generate a preliminary action timing arrangement. This process can use the timestamp mechanism to assign an accurate execution time point to each sub-task, for example, the “lighting system turning on” task can be executed at timestamp T1, and the “camera starting recording” task can be executed at timestamp T2. Based on the preliminary action timing arrangement, use timing optimization technology to finely adjust the action gap and response time to generate an accurate action timing arrangement. This process can be completed through genetic algorithm or particle swarm optimization algorithm to ensure that the action gap is minimized and the response time meets the requirements.

[0058] In some cases, a sudden event signal may be detected in the park, such as a fire alarm or an intrusion alarm. At this time, proceed to step S6, extract priority information from the event response attribute, adjust the frequency parameter in the timing arrangement using a dynamic scheduling method, and generate an optimized control response sequence. First, if a sudden event signal is detected in the action timing arrangement, start the event response module, extract priority information from the event response attribute, for example, the priority of the fire alarm signal is higher than that of the ordinary lighting task. In step S602, based on the priority information, use a dynamic scheduling method to adjust the frequency parameter in the timing arrangement in real time to generate a preliminary control response sequence. This process can use a pre-emptive scheduling algorithm to insert high-priority tasks into the current timing arrangement and adjust the execution time of low-priority tasks, for example, when a fire alarm signal is triggered, the lighting system task can be paused and the start task of the fire-fighting device can be executed first. Finally, based on the preliminary control response sequence, use priority optimization technology to optimize the task execution logic of the control response sequence to generate an optimized control response sequence. This process can be completed through simulated annealing algorithm to ensure that the task execution order meets the priority requirements and reduces resource waste as much as possible.

[0059] After generating the optimized control response sequence, enter step S7, based on the optimized control response sequence, verify the data transmission of the interconnection channel of the park equipment, judge whether the transmission delay is within the acceptable range, if not, reassign the transmission path through the channel optimization module, generate stable data interaction flow. First, based on the optimized control response sequence, verify the data transmission of the interconnection channel of the park equipment, through the delay monitoring technology, judge whether the transmission delay is within the acceptable range, for example, assume that the delay of a data packet from a security camera to the central control center is 500 milliseconds, if the acceptable range is 300 milliseconds, then the transmission delay is out of range. Then if the transmission delay exceeds the acceptable range, start the channel optimization module, reassign the transmission path, generate a preliminary data interaction flow, this process can use Dijkstra algorithm or algorithm to find the optimal transmission path, ensure that the data packet can quickly reach the destination, for example, the data packet originally transmitted through the wireless network can be switched to the wired network transmission to reduce the delay. Finally, based on the preliminary data interaction flow, use the path stability analysis method to evaluate the reliability and delay optimization effect of the transmission path, generate a stable data interaction flow, this process can evaluate the path stability through the packet loss rate and delay jitter index, ensure the reliability of the data interaction flow.

[0060] Finally, enter step S8, monitor the overall coordination efficiency index from the stable data interaction flow, use the feedback loop mechanism to iteratively update the frequency sequence and conversion parameters, generate the final running stable state. First, monitor the overall coordination efficiency index from the stable data interaction flow, through the efficiency evaluation technology, analyze the change trend of the coordination efficiency, for example, the throughput, response time and resource utilization can be used to evaluate the coordination efficiency. Then, based on the change trend of the coordination efficiency, use the feedback loop mechanism to iteratively update the frequency sequence and conversion parameters, generate a preliminary running stable state, this process can be completed through the adaptive control algorithm, for example, dynamically adjust the frequency sequence according to the change of the throughput, or optimize the conversion parameters according to the change of the resource utilization. Finally, based on the preliminary running stable state, use the stability verification method to evaluate the persistence and reliability of the running stable state, generate the final running stable state, this process can be completed through long-time running test, ensure that the system can maintain stable operation under various working conditions.

[0061] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not limited to the scope of protection of the present application, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalent, without departing from the essence and scope of the technical solutions of the present application.

Claims

1. An adaptive protocol conversion and collaborative control method for smart park equipment, characterized in that: Includes the following steps: S1. Based on the communication protocol type and operating frequency data of all devices in the park, extract protocol format features and frequency cycle parameters to generate an initial set of device attributes; S2. Based on the initial set of device attributes, cluster analysis is used to group the protocol types and determine the conversion rule mapping relationship between each group of protocols. S3. If there is a new device protocol that is not covered by the conversion rule mapping relationship, the data packets of the new device are decoded and feature matched in real time through the protocol parsing module to obtain dynamically adapted conversion parameters. S4. By comparing the actual value and theoretical value of the equipment operating cycle, calculate the magnitude of the deviation value and determine whether the deviation value exceeds the preset threshold. If it exceeds the threshold, use the frequency adjustment algorithm to synchronously calibrate the operating cycle of the equipment with collaborative task association and generate a coordinated frequency sequence. S5. Based on the coordinated frequency sequence, obtain the collaborative task requirements between related devices, and apply the sequence to the device control instructions through the task allocation module to generate a precise action timing arrangement. S6. If a sudden event signal is detected in the action sequence arrangement, the priority is determined according to the event type of the sudden event, and the frequency parameters in the sequence arrangement are adjusted by dynamic scheduling method to generate an optimized control response sequence. S7. Based on the optimized control response sequence, the data transmission of the interconnection channel of the park equipment is verified to determine whether the transmission delay is within an acceptable range. If not, the transmission path is reallocated through the channel optimization module to generate a stable data interaction flow. S8. Monitor the overall collaborative efficiency index from the stable data interaction flow, and use a feedback loop mechanism to iteratively update the frequency sequence and conversion parameters to generate the final stable operating state.

2. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: The specific steps for generating an initial set of device attributes by extracting protocol format features and frequency cycle parameters based on the communication protocol types and operating frequency data of all devices in the park are as follows: Based on the communication protocol type and operating frequency data of the equipment in the park, a multi-dimensional data parsing method is used to identify the protocol field structure and frequency cycle characteristics, extract the protocol format features and frequency cycle parameters, and generate a preliminary set of equipment attributes. Based on the preliminary set of device attributes, pattern recognition technology is used to deeply mine the semantic information and frequency cycle fluctuation patterns of the protocol fields, and generate a detailed description of the protocol format features and frequency cycle parameters. Based on the detailed description of the protocol format features and frequency cycle parameters, a hierarchical classification method is adopted to organize the attribute set in layers according to the functional category and operating characteristics of the device, thereby generating an initial set of device attributes.

3. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: Based on the initial set of device attributes, the specific steps for grouping protocol types using cluster analysis and determining the conversion rule mapping relationship between each group of protocols are as follows: Based on the initial set of device attributes, a clustering analysis method is used to group the protocol types according to the similarity of protocol format features and the correlation of frequency period parameters, generating protocol grouping results. Based on the protocol grouping results, rule derivation technology is used to analyze the field mapping relationship and data packet structure conversion logic between each group of protocols, and generate protocol conversion rule mapping relationship; Based on the aforementioned protocol conversion rule mapping relationship, a consistency verification method is used to test and optimize the accuracy and applicability of the mapping relationship to ensure the effectiveness of the protocol conversion.

4. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: If a new device protocol exists that is not covered by the aforementioned conversion rule mapping relationship, the specific steps for obtaining dynamically adapted conversion parameters by real-time decoding and feature matching of the new device's data packets through the protocol parsing module are as follows: If there is a new device protocol that is not covered by the conversion rule mapping relationship, start the protocol parsing module to decode the data packets of the new device in real time and extract the key fields and communication features of the data packets. Based on the aforementioned key fields and communication characteristics, feature matching technology is used to compare the new device protocol with existing protocols and generate dynamically adapted conversion parameters. Based on the dynamically adapted conversion parameters, the protocol compatibility evaluation method is used to verify and optimize the adaptation effect of the new device protocol, ensuring seamless connection of protocol conversion.

5. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: By comparing the actual and theoretical values ​​of the equipment's operating cycle, the deviation is calculated, and it is determined whether the deviation exceeds a preset threshold. If it does, a frequency adjustment algorithm is used to synchronize and calibrate the operating cycles of equipment with collaborative task associations, generating a coordinated frequency sequence. The specific steps are as follows: The deviation value of the frequency cycle parameter is extracted from the dynamically adapted conversion parameters, and the magnitude of the deviation value is calculated by comparing the actual value of the equipment operating cycle with the theoretical value. Determine whether the deviation value exceeds a preset threshold. If it does, start the frequency adjustment algorithm to synchronously calibrate the operating cycle of the relevant equipment and generate a preliminary frequency sequence. Based on the preliminary frequency sequence, frequency stability analysis is used to evaluate and optimize the fluctuation of the frequency sequence, generating a coordinated frequency sequence.

6. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: Based on the coordinated frequency sequence, the collaborative task requirements between associated devices are obtained. The sequence is then applied to the device control commands through the task allocation module to generate a precise action timing arrangement. The specific steps are as follows: Based on the coordinated frequency sequence, the collaborative task requirements between associated devices are obtained, and the collaborative task is decomposed into multiple sub-tasks through task decomposition technology. Based on the sub-tasks, a task allocation module is used to match the frequency sequence with the device control commands to generate a preliminary action timing arrangement. Based on the preliminary action timing arrangement, timing optimization technology is used to finely adjust the action gaps and response times to generate an accurate action timing arrangement.

7. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: If a sudden event signal is detected in the action sequence, the priority is determined according to the event type of the sudden event, and the frequency parameters in the sequence are adjusted using a dynamic scheduling method to generate an optimized control response sequence. The specific steps are as follows: If a sudden event signal is detected in the action sequence arrangement, the priority is determined according to the event type of the sudden event; Based on the aforementioned priority, a dynamic scheduling method is used to adjust the frequency parameters in the timing arrangement in real time to generate a preliminary control response sequence. Based on the preliminary control response sequence, priority optimization techniques are used to optimize the task execution logic of the control response sequence, generating an optimized control response sequence.

8. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: Based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment to determine whether the transmission delay is within an acceptable range. If not, the transmission path is reallocated through the channel optimization module to generate a stable data interaction stream. The specific steps are as follows: Based on the optimized control response sequence, data transmission is verified on the interconnection channel of the park equipment. Through delay monitoring technology, it is determined whether the transmission delay is within an acceptable range. If the transmission delay exceeds the acceptable range, the channel optimization module is activated to reallocate the transmission path and generate an initial data interaction stream. Based on the initial data interaction flow, a path stability analysis method is used to evaluate the reliability and delay optimization effect of the transmission path, and generate a stable data interaction flow.

9. The adaptive protocol conversion and collaborative control method for smart park equipment according to claim 1, characterized in that: The specific steps for monitoring overall collaborative efficiency indicators from a stable data interaction stream, iteratively updating the frequency sequence and transformation parameters using a feedback loop mechanism, and generating the final stable operating state are as follows: Monitor overall collaboration efficiency indicators from stable data interaction flows, and analyze the changing trends of collaboration efficiency through efficiency evaluation techniques; Based on the aforementioned trend of collaborative efficiency change, a feedback loop mechanism is adopted to iteratively update the frequency sequence and conversion parameters to generate a preliminary stable operating state. Based on the preliminary stable operating state, the sustainability and reliability of the stable operating state are evaluated using stability verification methods to generate the final stable operating state.

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